---
_id: '13053'
abstract:
- lang: eng
text: 'Deep neural networks (DNNs) often have to be compressed, via pruning and/or
quantization, before they can be deployed in practical settings. In this work
we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization
step in a principled way, in order to produce models whose local loss behavior
is stable under compression operations such as pruning. Thus, dense models trained
via CrAM should be compressible post-training, in a single step, without significant
accuracy loss. Experimental results on standard benchmarks, such as residual networks
for ImageNet classification and BERT models for language modelling, show that
CrAM produces dense models that can be more accurate than the standard SGD/Adam-based
baselines, but which are stable under weight pruning: specifically, we can prune
models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90%
with reasonable (∼1%) accuracy loss, which is competitive with gradual compression
methods. Additionally, CrAM can produce sparse models which perform well for transfer
learning, and it also works for semi-structured 2:4 pruning patterns supported
by GPU hardware. The code for reproducing the results is available at this https
URL .'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "AP, EK, DA received funding from the European Research Council (ERC)
under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant
agreement No 805223 ScaleML). AV acknowledges the support of the French Agence Nationale
de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT). We further
acknowledge the support from the Scientific Service Units (SSU) of ISTA through
resources provided by Scientific Computing (SciComp)-"
article_processing_charge: No
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Adrian
full_name: Vladu, Adrian
last_name: Vladu
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware
Minimizer. In: 11th International Conference on Learning Representations .'
apa: 'Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.).
CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning
Representations . Kigali, Rwanda .'
chicago: 'Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert,
and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International
Conference on Learning Representations , n.d.'
ieee: 'E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM:
A Compression-Aware Minimizer,” in 11th International Conference on Learning
Representations , Kigali, Rwanda .'
ista: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware
Minimizer. 11th International Conference on Learning Representations . ICLR: International
Conference on Learning Representations.'
mla: 'Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th
International Conference on Learning Representations .'
short: E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International
Conference on Learning Representations , n.d.
conference:
end_date: 2023-05-05
location: 'Kigali, Rwanda '
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-05-23T11:36:18Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-06-01T12:54:45Z
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '2207.14200'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/pdf?id=_eTZBs-yedr
month: '05'
oa: 1
oa_version: Preprint
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: '11th International Conference on Learning Representations '
publication_status: accepted
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
status: public
title: 'CrAM: A Compression-Aware Minimizer'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '13074'
abstract:
- lang: eng
text: "Deep learning has become an integral part of a large number of important
applications, and many of the recent breakthroughs have been enabled by the ability
to train very large models, capable to capture complex patterns and relationships
from the data. At the same time, the massive sizes of modern deep learning models
have made their deployment to smaller devices more challenging; this is particularly
important, as in many applications the users rely on accurate deep learning predictions,
but they only have access to devices with limited memory and compute power. One
solution to this problem is to prune neural networks, by setting as many of their
parameters as possible to zero, to obtain accurate sparse models with lower memory
footprint. Despite the great research progress in obtaining sparse models that
preserve accuracy, while satisfying memory and computational constraints, there
are still many challenges associated with efficiently training sparse models,
as well as understanding their generalization properties.\r\n\r\nThe focus of
this thesis is to investigate how the training process of sparse models can be
made more efficient, and to understand the differences between sparse and dense
models in terms of how well they can generalize to changes in the data distribution.
We first study a method for co-training sparse and dense models, at a lower cost
compared to regular training. With our method we can obtain very accurate sparse
networks, and dense models that can recover the baseline accuracy. Furthermore,
we are able to more easily analyze the differences, at prediction level, between
the sparse-dense model pairs. Next, we investigate the generalization properties
of sparse neural networks in more detail, by studying how well different sparse
models trained on a larger task can adapt to smaller, more specialized tasks,
in a transfer learning scenario. Our analysis across multiple pruning methods
and sparsity levels reveals that sparse models provide features that can transfer
similarly to or better than the dense baseline. However, the choice of the pruning
method plays an important role, and can influence the results when the features
are fixed (linear finetuning), or when they are allowed to adapt to the new task
(full finetuning). Using sparse models with fixed masks for finetuning on new
tasks has an important practical advantage, as it enables training neural networks
on smaller devices. However, one drawback of current pruning methods is that the
entire training cycle has to be repeated to obtain the initial sparse model, for
every sparsity target; in consequence, the entire training process is costly and
also multiple models need to be stored. In the last part of the thesis we propose
a method that can train accurate dense models that are compressible in a single
step, to multiple sparsity levels, without additional finetuning. Our method results
in sparse models that can be competitive with existing pruning methods, and which
can also successfully generalize to new tasks."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
citation:
ama: Peste E-A. Efficiency and generalization of sparse neural networks. 2023. doi:10.15479/at:ista:13074
apa: Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
chicago: Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural
Networks.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13074.
ieee: E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute
of Science and Technology Austria, 2023.
ista: Peste E-A. 2023. Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria.
mla: Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
short: E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute
of Science and Technology Austria, 2023.
date_created: 2023-05-23T17:07:53Z
date_published: 2023-05-23T00:00:00Z
date_updated: 2023-08-04T10:33:27Z
day: '23'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
doi: 10.15479/at:ista:13074
ec_funded: 1
file:
- access_level: open_access
checksum: 6b3354968403cb9d48cc5a83611fb571
content_type: application/pdf
creator: epeste
date_created: 2023-05-24T16:11:16Z
date_updated: 2023-05-24T16:11:16Z
file_id: '13087'
file_name: PhD_Thesis_Alexandra_Peste_final.pdf
file_size: 2152072
relation: main_file
success: 1
- access_level: closed
checksum: 8d0df94bbcf4db72c991f22503b3fd60
content_type: application/zip
creator: epeste
date_created: 2023-05-24T16:12:59Z
date_updated: 2023-05-24T16:12:59Z
file_id: '13088'
file_name: PhD_Thesis_APeste.zip
file_size: 1658293
relation: source_file
file_date_updated: 2023-05-24T16:12:59Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '147'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11458'
relation: part_of_dissertation
status: public
- id: '13053'
relation: part_of_dissertation
status: public
- id: '12299'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
title: Efficiency and generalization of sparse neural networks
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '14320'
abstract:
- lang: eng
text: The development of two-dimensional materials has resulted in a diverse range
of novel, high-quality compounds with increasing complexity. A key requirement
for a comprehensive quantitative theory is the accurate determination of these
materials' band structure parameters. However, this task is challenging due to
the intricate band structures and the indirect nature of experimental probes.
In this work, we introduce a general framework to derive band structure parameters
from experimental data using deep neural networks. We applied our method to the
penetration field capacitance measurement of trilayer graphene, an effective probe
of its density of states. First, we demonstrate that a trained deep network gives
accurate predictions for the penetration field capacitance as a function of tight-binding
parameters. Next, we use the fast and accurate predictions from the trained network
to automatically determine tight-binding parameters directly from experimental
data, with extracted parameters being in a good agreement with values in the literature.
We conclude by discussing potential applications of our method to other materials
and experimental techniques beyond penetration field capacitance.
acknowledgement: A.F.Y. acknowledges primary support from the Department of Energy
under award DE-SC0020043, and additional support from the Gordon and Betty Moore
Foundation under award GBMF9471 for group operations.
article_number: '125411'
article_processing_charge: No
article_type: original
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Areg
full_name: Ghazaryan, Areg
id: 4AF46FD6-F248-11E8-B48F-1D18A9856A87
last_name: Ghazaryan
orcid: 0000-0001-9666-3543
- first_name: Alexander A.
full_name: Zibrov, Alexander A.
last_name: Zibrov
- first_name: Andrea F.
full_name: Young, Andrea F.
last_name: Young
- first_name: Maksym
full_name: Serbyn, Maksym
id: 47809E7E-F248-11E8-B48F-1D18A9856A87
last_name: Serbyn
orcid: 0000-0002-2399-5827
citation:
ama: 'Henderson PM, Ghazaryan A, Zibrov AA, Young AF, Serbyn M. Deep learning extraction
of band structure parameters from density of states: A case study on trilayer
graphene. Physical Review B. 2023;108(12). doi:10.1103/physrevb.108.125411'
apa: 'Henderson, P. M., Ghazaryan, A., Zibrov, A. A., Young, A. F., & Serbyn,
M. (2023). Deep learning extraction of band structure parameters from density
of states: A case study on trilayer graphene. Physical Review B. American
Physical Society. https://doi.org/10.1103/physrevb.108.125411'
chicago: 'Henderson, Paul M, Areg Ghazaryan, Alexander A. Zibrov, Andrea F. Young,
and Maksym Serbyn. “Deep Learning Extraction of Band Structure Parameters from
Density of States: A Case Study on Trilayer Graphene.” Physical Review B.
American Physical Society, 2023. https://doi.org/10.1103/physrevb.108.125411.'
ieee: 'P. M. Henderson, A. Ghazaryan, A. A. Zibrov, A. F. Young, and M. Serbyn,
“Deep learning extraction of band structure parameters from density of states:
A case study on trilayer graphene,” Physical Review B, vol. 108, no. 12.
American Physical Society, 2023.'
ista: 'Henderson PM, Ghazaryan A, Zibrov AA, Young AF, Serbyn M. 2023. Deep learning
extraction of band structure parameters from density of states: A case study on
trilayer graphene. Physical Review B. 108(12), 125411.'
mla: 'Henderson, Paul M., et al. “Deep Learning Extraction of Band Structure Parameters
from Density of States: A Case Study on Trilayer Graphene.” Physical Review
B, vol. 108, no. 12, 125411, American Physical Society, 2023, doi:10.1103/physrevb.108.125411.'
short: P.M. Henderson, A. Ghazaryan, A.A. Zibrov, A.F. Young, M. Serbyn, Physical
Review B 108 (2023).
date_created: 2023-09-12T07:12:12Z
date_published: 2023-09-15T00:00:00Z
date_updated: 2023-09-20T09:38:24Z
day: '15'
department:
- _id: MaSe
- _id: ChLa
- _id: MiLe
doi: 10.1103/physrevb.108.125411
external_id:
arxiv:
- '2210.06310'
intvolume: ' 108'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.06310
month: '09'
oa: 1
oa_version: Preprint
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Deep learning extraction of band structure parameters from density of states:
A case study on trilayer graphene'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 108
year: '2023'
...
---
_id: '14410'
abstract:
- lang: eng
text: This paper focuses on the implementation details of the baseline methods and
a recent lightweight conditional model extrapolation algorithm LIMES [5] for streaming
data under class-prior shift. LIMES achieves superior performance over the baseline
methods, especially concerning the minimum-across-day accuracy, which is important
for the users of the system. In this work, the key measures to facilitate reproducibility
and enhance the credibility of the results are described.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Paulina
full_name: Tomaszewska, Paulina
last_name: Tomaszewska
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Tomaszewska P, Lampert C. On the implementation of baselines and lightweight
conditional model extrapolation (LIMES) under class-prior shift. In: International
Workshop on Reproducible Research in Pattern Recognition. Vol 14068. Springer
Nature; 2023:67-73. doi:10.1007/978-3-031-40773-4_6'
apa: 'Tomaszewska, P., & Lampert, C. (2023). On the implementation of baselines
and lightweight conditional model extrapolation (LIMES) under class-prior shift.
In International Workshop on Reproducible Research in Pattern Recognition
(Vol. 14068, pp. 67–73). Montreal, Canada: Springer Nature. https://doi.org/10.1007/978-3-031-40773-4_6'
chicago: Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines
and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.”
In International Workshop on Reproducible Research in Pattern Recognition,
14068:67–73. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-40773-4_6.
ieee: P. Tomaszewska and C. Lampert, “On the implementation of baselines and lightweight
conditional model extrapolation (LIMES) under class-prior shift,” in International
Workshop on Reproducible Research in Pattern Recognition, Montreal, Canada,
2023, vol. 14068, pp. 67–73.
ista: 'Tomaszewska P, Lampert C. 2023. On the implementation of baselines and lightweight
conditional model extrapolation (LIMES) under class-prior shift. International
Workshop on Reproducible Research in Pattern Recognition. RRPR: Reproducible Research
in Pattern Recognition, LNCS, vol. 14068, 67–73.'
mla: Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines
and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.”
International Workshop on Reproducible Research in Pattern Recognition,
vol. 14068, Springer Nature, 2023, pp. 67–73, doi:10.1007/978-3-031-40773-4_6.
short: P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research
in Pattern Recognition, Springer Nature, 2023, pp. 67–73.
conference:
end_date: 2022-08-21
location: Montreal, Canada
name: 'RRPR: Reproducible Research in Pattern Recognition'
start_date: 2022-08-21
date_created: 2023-10-08T22:01:18Z
date_published: 2023-08-20T00:00:00Z
date_updated: 2023-10-09T06:48:02Z
day: '20'
department:
- _id: ChLa
doi: 10.1007/978-3-031-40773-4_6
intvolume: ' 14068'
language:
- iso: eng
month: '08'
oa_version: None
page: 67-73
publication: International Workshop on Reproducible Research in Pattern Recognition
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031407727'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the implementation of baselines and lightweight conditional model extrapolation
(LIMES) under class-prior shift
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14068
year: '2023'
...
---
_id: '14446'
abstract:
- lang: eng
text: Recent work has paid close attention to the first principle of Granger causality,
according to which cause precedes effect. In this context, the question may arise
whether the detected direction of causality also reverses after the time reversal
of unidirectionally coupled data. Recently, it has been shown that for unidirectionally
causally connected autoregressive (AR) processes X → Y, after time reversal of
data, the opposite causal direction Y → X is indeed detected, although typically
as part of the bidirectional X↔ Y link. As we argue here, the answer is different
when the measured data are not from AR processes but from linked deterministic
systems. When the goal is the usual forward data analysis, cross-mapping-like
approaches correctly detect X → Y, while Granger causality-like approaches, which
should not be used for deterministic time series, detect causal independence X
→ Y. The results of backward causal analysis depend on the predictability of the
reversed data. Unlike AR processes, observables from deterministic dynamical systems,
even complex nonlinear ones, can be predicted well forward, while backward predictions
can be difficult (notably when the time reversal of a function leads to one-to-many
relations). To address this problem, we propose an approach based on models that
provide multiple candidate predictions for the target, combined with a loss function
that consideres only the best candidate. The resulting good forward and backward
predictability supports the view that unidirectionally causally linked deterministic
dynamical systems X → Y can be expected to detect the same link both before and
after time reversal.
acknowledgement: The work was supported by the Scientific Grant Agency of the Ministry
of Education of the Slovak Republic and the Slovak Academy of Sciences, projects
APVV-21-0216, VEGA2-0096-21 and VEGA 2-0023-22.
article_processing_charge: Yes
article_type: original
author:
- first_name: Jozef
full_name: Jakubík, Jozef
last_name: Jakubík
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Martina
full_name: Chvosteková, Martina
last_name: Chvosteková
- first_name: Anna
full_name: Krakovská, Anna
last_name: Krakovská
citation:
ama: Jakubík J, Phuong M, Chvosteková M, Krakovská A. Against the flow of time with
multi-output models. Measurement Science Review. 2023;23(4):175-183. doi:10.2478/msr-2023-0023
apa: Jakubík, J., Phuong, M., Chvosteková, M., & Krakovská, A. (2023). Against
the flow of time with multi-output models. Measurement Science Review.
Sciendo. https://doi.org/10.2478/msr-2023-0023
chicago: Jakubík, Jozef, Mary Phuong, Martina Chvosteková, and Anna Krakovská. “Against
the Flow of Time with Multi-Output Models.” Measurement Science Review.
Sciendo, 2023. https://doi.org/10.2478/msr-2023-0023.
ieee: J. Jakubík, M. Phuong, M. Chvosteková, and A. Krakovská, “Against the flow
of time with multi-output models,” Measurement Science Review, vol. 23,
no. 4. Sciendo, pp. 175–183, 2023.
ista: Jakubík J, Phuong M, Chvosteková M, Krakovská A. 2023. Against the flow of
time with multi-output models. Measurement Science Review. 23(4), 175–183.
mla: Jakubík, Jozef, et al. “Against the Flow of Time with Multi-Output Models.”
Measurement Science Review, vol. 23, no. 4, Sciendo, 2023, pp. 175–83,
doi:10.2478/msr-2023-0023.
short: J. Jakubík, M. Phuong, M. Chvosteková, A. Krakovská, Measurement Science
Review 23 (2023) 175–183.
date_created: 2023-10-22T22:01:15Z
date_published: 2023-08-01T00:00:00Z
date_updated: 2023-10-31T12:12:47Z
day: '01'
ddc:
- '510'
department:
- _id: ChLa
doi: 10.2478/msr-2023-0023
file:
- access_level: open_access
checksum: b069cc10fa6a7c96b2bc9f728165f9e6
content_type: application/pdf
creator: dernst
date_created: 2023-10-31T12:07:23Z
date_updated: 2023-10-31T12:07:23Z
file_id: '14476'
file_name: 2023_MeasurementScienceRev_Jakubik.pdf
file_size: 2639783
relation: main_file
success: 1
file_date_updated: 2023-10-31T12:07:23Z
has_accepted_license: '1'
intvolume: ' 23'
issue: '4'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 175-183
publication: Measurement Science Review
publication_identifier:
eissn:
- 1335-8871
publication_status: published
publisher: Sciendo
quality_controlled: '1'
scopus_import: '1'
status: public
title: Against the flow of time with multi-output models
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 23
year: '2023'
...
---
_id: '14771'
abstract:
- lang: eng
text: Pruning—that is, setting a significant subset of the parameters of a neural
network to zero—is one of the most popular methods of model compression. Yet,
several recent works have raised the issue that pruning may induce or exacerbate
bias in the output of the compressed model. Despite existing evidence for this
phenomenon, the relationship between neural network pruning and induced bias is
not well-understood. In this work, we systematically investigate and characterize
this phenomenon in Convolutional Neural Networks for computer vision. First, we
show that it is in fact possible to obtain highly-sparse models, e.g. with less
than 10% remaining weights, which do not decrease in accuracy nor substantially
increase in bias when compared to dense models. At the same time, we also find
that, at higher sparsities, pruned models exhibit higher uncertainty in their
outputs, as well as increased correlations, which we directly link to increased
bias. We propose easy-to-use criteria which, based only on the uncompressed model,
establish whether bias will increase with pruning, and identify the samples most
susceptible to biased predictions post-compression. Our code can be found at https://github.com/IST-DASLab/pruned-vision-model-bias.
acknowledgement: The authors would like to sincerely thank Sara Hooker for her feedback
during the development of this work. EI was supported in part by the FWF DK VGSCO,
grant agreement number W1260-N35. AP and DA acknowledge generous ERC support, via
Starting Grant 805223 ScaleML.
article_processing_charge: No
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Iofinova EB, Peste E-A, Alistarh D-A. Bias in pruned vision models: In-depth
analysis and countermeasures. In: 2023 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. IEEE; 2023:24364-24373. doi:10.1109/cvpr52729.2023.02334'
apa: 'Iofinova, E. B., Peste, E.-A., & Alistarh, D.-A. (2023). Bias in pruned
vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC,
Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334'
chicago: 'Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias
in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.
https://doi.org/10.1109/cvpr52729.2023.02334.'
ieee: 'E. B. Iofinova, E.-A. Peste, and D.-A. Alistarh, “Bias in pruned vision models:
In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.'
ista: 'Iofinova EB, Peste E-A, Alistarh D-A. 2023. Bias in pruned vision models:
In-depth analysis and countermeasures. 2023 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition,
24364–24373.'
mla: 'Iofinova, Eugenia B., et al. “Bias in Pruned Vision Models: In-Depth Analysis
and Countermeasures.” 2023 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, IEEE, 2023, pp. 24364–73, doi:10.1109/cvpr52729.2023.02334.'
short: E.B. Iofinova, E.-A. Peste, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.
conference:
end_date: 2023-06-24
location: Vancouver, BC, Canada
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2023-06-17
date_created: 2024-01-10T08:42:40Z
date_published: 2023-08-22T00:00:00Z
date_updated: 2024-01-10T08:59:26Z
day: '22'
department:
- _id: DaAl
- _id: ChLa
doi: 10.1109/cvpr52729.2023.02334
ec_funded: 1
external_id:
arxiv:
- '2304.12622'
isi:
- '001062531308068'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.12622
month: '08'
oa: 1
oa_version: Preprint
page: 24364-24373
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: ' W1260-N35'
name: Vienna Graduate School on Computational Optimization
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eisbn:
- '9798350301298'
eissn:
- 2575-7075
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/pruned-vision-model-bias
status: public
title: 'Bias in pruned vision models: In-depth analysis and countermeasures'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14921'
abstract:
- lang: eng
text: Neural collapse (NC) refers to the surprising structure of the last layer
of deep neural networks in the terminal phase of gradient descent training. Recently,
an increasing amount of experimental evidence has pointed to the propagation of
NC to earlier layers of neural networks. However, while the NC in the last layer
is well studied theoretically, much less is known about its multi-layered counterpart
- deep neural collapse (DNC). In particular, existing work focuses either on linear
layers or only on the last two layers at the price of an extra assumption. Our
paper fills this gap by generalizing the established analytical framework for
NC - the unconstrained features model - to multiple non-linear layers. Our key
technical contribution is to show that, in a deep unconstrained features model,
the unique global optimum for binary classification exhibits all the properties
typical of DNC. This explains the existing experimental evidence of DNC. We also
empirically show that (i) by optimizing deep unconstrained features models via
gradient descent, the resulting solution agrees well with our theory, and (ii)
trained networks recover the unconstrained features suitable for the occurrence
of DNC, thus supporting the validity of this modeling principle.
acknowledgement: M. M. is partially supported by the 2019 Lopez-Loreta Prize. The
authors would like to thank Eugenia Iofinova, Bernd Prach and Simone Bombari for
valuable feedback on the manuscript.
alternative_title:
- NeurIPS
article_processing_charge: No
author:
- first_name: Peter
full_name: Súkeník, Peter
id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c
last_name: Súkeník
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal
for the deep unconstrained features model. In: 37th Annual Conference on Neural
Information Processing Systems.'
apa: Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). Deep neural collapse is
provably optimal for the deep unconstrained features model. In 37th Annual
Conference on Neural Information Processing Systems. New Orleans, LA, United
States.
chicago: Súkeník, Peter, Marco Mondelli, and Christoph Lampert. “Deep Neural Collapse
Is Provably Optimal for the Deep Unconstrained Features Model.” In 37th Annual
Conference on Neural Information Processing Systems, n.d.
ieee: P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably
optimal for the deep unconstrained features model,” in 37th Annual Conference
on Neural Information Processing Systems, New Orleans, LA, United States.
ista: 'Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal
for the deep unconstrained features model. 37th Annual Conference on Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems, NeurIPS, .'
mla: Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep
Unconstrained Features Model.” 37th Annual Conference on Neural Information
Processing Systems.
short: P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural
Information Processing Systems, n.d.
conference:
end_date: 2023-12-16
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2023-12-10
date_created: 2024-02-02T11:17:41Z
date_published: 2023-12-15T00:00:00Z
date_updated: 2024-02-06T07:53:26Z
day: '15'
department:
- _id: MaMo
- _id: ChLa
external_id:
arxiv:
- '2305.13165'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2305.13165'
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 37th Annual Conference on Neural Information Processing Systems
publication_status: inpress
quality_controlled: '1'
status: public
title: Deep neural collapse is provably optimal for the deep unconstrained features
model
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '15039'
abstract:
- lang: eng
text: 'A crucial property for achieving secure, trustworthy and interpretable deep
learning systems is their robustness: small changes to a system''s inputs should
not result in large changes to its outputs. Mathematically, this means one strives
for networks with a small Lipschitz constant. Several recent works have focused
on how to construct such Lipschitz networks, typically by imposing constraints
on the weight matrices. In this work, we study an orthogonal aspect, namely the
role of the activation function. We show that commonly used activation functions,
such as MaxMin, as well as all piece-wise linear ones with two segments unnecessarily
restrict the class of representable functions, even in the simplest one-dimensional
setting. We furthermore introduce the new N-activation function that is provably
more expressive than currently popular activation functions. We provide code at
this https URL.'
article_number: '2311.06103'
article_processing_charge: No
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations.
arXiv. doi:10.48550/ARXIV.2311.06103
apa: Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive
with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
chicago: Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More
Expressive with N-Activations.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2311.06103.
ieee: B. Prach and C. Lampert, “1-Lipschitz neural networks are more expressive
with N-activations,” arXiv. .
ista: Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations.
arXiv, 2311.06103.
mla: Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More
Expressive with N-Activations.” ArXiv, 2311.06103, doi:10.48550/ARXIV.2311.06103.
short: B. Prach, C. Lampert, ArXiv (n.d.).
date_created: 2024-02-28T17:59:32Z
date_published: 2023-11-10T00:00:00Z
date_updated: 2024-03-04T07:02:39Z
day: '10'
department:
- _id: GradSch
- _id: ChLa
doi: 10.48550/ARXIV.2311.06103
external_id:
arxiv:
- '2311.06103'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.06103
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 1-Lipschitz neural networks are more expressive with N-activations
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12660'
abstract:
- lang: eng
text: 'We present Cross-Client Label Propagation(XCLP), a new method for transductive
federated learning. XCLP estimates a data graph jointly from the data of multiple
clients and computes labels for the unlabeled data by propagating label information
across the graph. To avoid clients having to share their data with anyone, XCLP
employs two cryptographically secure protocols: secure Hamming distance computation
and secure summation. We demonstrate two distinct applications of XCLP within
federated learning. In the first, we use it in a one-shot way to predict labels
for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled
training data in a federated semi-supervised setting. Experiments on both real
federated and standard benchmark datasets show that in both applications XCLP
achieves higher classification accuracy than alternative approaches.'
article_number: '2210.06434'
article_processing_charge: No
author:
- first_name: Jonathan A
full_name: Scott, Jonathan A
id: e499926b-f6e0-11ea-865d-9c63db0031e8
last_name: Scott
- first_name: Michelle X
full_name: Yeo, Michelle X
id: 2D82B818-F248-11E8-B48F-1D18A9856A87
last_name: Yeo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive
federated learning. arXiv. doi:10.48550/arXiv.2210.06434
apa: Scott, J. A., Yeo, M. X., & Lampert, C. (n.d.). Cross-client Label Propagation
for transductive federated learning. arXiv. https://doi.org/10.48550/arXiv.2210.06434
chicago: Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client
Label Propagation for Transductive Federated Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2210.06434.
ieee: J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for
transductive federated learning,” arXiv. .
ista: Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive
federated learning. arXiv, 2210.06434.
mla: Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive
Federated Learning.” ArXiv, 2210.06434, doi:10.48550/arXiv.2210.06434.
short: J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).
date_created: 2023-02-20T08:21:50Z
date_published: 2022-10-12T00:00:00Z
date_updated: 2023-02-21T08:20:18Z
day: '12'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.48550/arXiv.2210.06434
external_id:
arxiv:
- '2210.06434'
file:
- access_level: open_access
checksum: 7ab20543fd4393f14fb857ce2e4f03c6
content_type: application/pdf
creator: chl
date_created: 2023-02-20T08:21:35Z
date_updated: 2023-02-20T08:21:35Z
file_id: '12661'
file_name: 2210.06434.pdf
file_size: 291893
relation: main_file
success: 1
file_date_updated: 2023-02-20T08:21:35Z
has_accepted_license: '1'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Cross-client Label Propagation for transductive federated learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12662'
abstract:
- lang: eng
text: 'Modern machine learning tasks often require considering not just one but
multiple objectives. For example, besides the prediction quality, this could be
the efficiency, robustness or fairness of the learned models, or any of their
combinations. Multi-objective learning offers a natural framework for handling
such problems without having to commit to early trade-offs. Surprisingly, statistical
learning theory so far offers almost no insight into the generalization properties
of multi-objective learning. In this work, we make first steps to fill this gap:
we establish foundational generalization bounds for the multi-objective setting
as well as generalization and excess bounds for learning with scalarizations.
We also provide the first theoretical analysis of the relation between the Pareto-optimal
sets of the true objectives and the Pareto-optimal sets of their empirical approximations
from training data. In particular, we show a surprising asymmetry: all Pareto-optimal
solutions can be approximated by empirically Pareto-optimal ones, but not vice
versa.'
article_number: '2208.13499'
article_processing_charge: No
author:
- first_name: Peter
full_name: Súkeník, Peter
id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c
last_name: Súkeník
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv.
doi:10.48550/arXiv.2208.13499
apa: Súkeník, P., & Lampert, C. (n.d.). Generalization in Multi-objective machine
learning. arXiv. https://doi.org/10.48550/arXiv.2208.13499
chicago: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective
Machine Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2208.13499.
ieee: P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,”
arXiv. .
ista: Súkeník P, Lampert C. Generalization in Multi-objective machine learning.
arXiv, 2208.13499.
mla: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine
Learning.” ArXiv, 2208.13499, doi:10.48550/arXiv.2208.13499.
short: P. Súkeník, C. Lampert, ArXiv (n.d.).
date_created: 2023-02-20T08:23:06Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2023-02-21T08:24:55Z
day: '29'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.48550/arXiv.2208.13499
external_id:
arxiv:
- '2208.13499'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2208.13499'
month: '08'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Generalization in Multi-objective machine learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12495'
abstract:
- lang: eng
text: "Fairness-aware learning aims at constructing classifiers that not only make
accurate predictions, but also do not discriminate against specific groups. It
is a fast-growing area of\r\nmachine learning with far-reaching societal impact.
However, existing fair learning methods\r\nare vulnerable to accidental or malicious
artifacts in the training data, which can cause\r\nthem to unknowingly produce
unfair classifiers. In this work we address the problem of\r\nfair learning from
unreliable training data in the robust multisource setting, where the\r\navailable
training data comes from multiple sources, a fraction of which might not be representative
of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat
identifies and suppresses those data sources that would have a negative impact
on\r\nfairness or accuracy if they were used for training. As such, FLEA is not
a replacement of\r\nprior fairness-aware learning methods but rather an augmentation
that makes any of them\r\nrobust against unreliable training data. We show the
effectiveness of our approach by a\r\ndiverse range of experiments on multiple
datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects
the learner against corruptions as long as the fraction of\r\naffected data sources
is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: 'The authors would like to thank Bernd Prach, Elias Frantar, Alexandra
Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research
was supported by the Scientific Service Units (SSU) of IST Austria through resources
provided by Scientific Computing (SciComp). This publication was made possible by
an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia
Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. '
article_processing_charge: No
article_type: original
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource
learning from unreliable training data. Transactions on Machine Learning Research.
2022.'
apa: 'Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably
robust fair multisource learning from unreliable training data. Transactions
on Machine Learning Research. ML Research Press.'
chicago: 'Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA:
Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions
on Machine Learning Research. ML Research Press, 2022.'
ieee: 'E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust
fair multisource learning from unreliable training data,” Transactions on Machine
Learning Research. ML Research Press, 2022.'
ista: 'Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair
multisource learning from unreliable training data. Transactions on Machine Learning
Research.'
mla: 'Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning
from Unreliable Training Data.” Transactions on Machine Learning Research,
ML Research Press, 2022.'
short: E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning
Research (2022).
date_created: 2023-02-02T20:29:57Z
date_published: 2022-12-22T00:00:00Z
date_updated: 2023-02-23T10:30:54Z
day: '22'
ddc:
- '000'
department:
- _id: ChLa
external_id:
arxiv:
- '2106.11732'
file:
- access_level: open_access
checksum: 97c8a8470759cab597abb973ca137a3b
content_type: application/pdf
creator: dernst
date_created: 2023-02-23T10:30:04Z
date_updated: 2023-02-23T10:30:04Z
file_id: '12673'
file_name: 2022_TMLR_Iofinova.pdf
file_size: 1948063
relation: main_file
success: 1
file_date_updated: 2023-02-23T10:30:04Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=XsPopigZXV
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: ' W1260-N35'
name: Vienna Graduate School on Computational Optimization
publication: Transactions on Machine Learning Research
publication_identifier:
issn:
- 2835-8856
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- description: source code
relation: software
url: https://github.com/ISTAustria-CVML/FLEA
status: public
title: 'FLEA: Provably robust fair multisource learning from unreliable training data'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '11839'
abstract:
- lang: eng
text: "It is a highly desirable property for deep networks to be robust against\r\nsmall
input changes. One popular way to achieve this property is by designing\r\nnetworks
with a small Lipschitz constant. In this work, we propose a new\r\ntechnique for
constructing such Lipschitz networks that has a number of\r\ndesirable properties:
it can be applied to any linear network layer\r\n(fully-connected or convolutional),
it provides formal guarantees on the\r\nLipschitz constant, it is easy to implement
and efficient to run, and it can be\r\ncombined with any training objective and
optimization method. In fact, our\r\ntechnique is the first one in the literature
that achieves all of these\r\nproperties simultaneously. Our main contribution
is a rescaling-based weight\r\nmatrix parametrization that guarantees each network
layer to have a Lipschitz\r\nconstant of at most 1 and results in the learned
weight matrices to be close to\r\northogonal. Hence we call such layers almost-orthogonal
Lipschitz (AOL).\r\nExperiments and ablation studies in the context of image classification
with\r\ncertified robust accuracy confirm that AOL layers achieve results that
are on\r\npar with most existing methods. Yet, they are simpler to implement and
more\r\nbroadly applicable, because they do not require computationally expensive\r\nmatrix
orthogonalization or inversion steps as part of the network\r\narchitecture. We
provide code at https://github.com/berndprach/AOL."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Prach B, Lampert C. Almost-orthogonal layers for efficient general-purpose
Lipschitz networks. In: Computer Vision – ECCV 2022. Vol 13681. Springer
Nature; 2022:350-365. doi:10.1007/978-3-031-19803-8_21'
apa: 'Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient
general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol.
13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21'
chicago: Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient
General-Purpose Lipschitz Networks.” In Computer Vision – ECCV 2022, 13681:350–65.
Springer Nature, 2022. https://doi.org/10.1007/978-3-031-19803-8_21.
ieee: B. Prach and C. Lampert, “Almost-orthogonal layers for efficient general-purpose
Lipschitz networks,” in Computer Vision – ECCV 2022, Tel Aviv, Israel,
2022, vol. 13681, pp. 350–365.
ista: 'Prach B, Lampert C. 2022. Almost-orthogonal layers for efficient general-purpose
Lipschitz networks. Computer Vision – ECCV 2022. ECCV: European Conference on
Computer Vision, LNCS, vol. 13681, 350–365.'
mla: Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient
General-Purpose Lipschitz Networks.” Computer Vision – ECCV 2022, vol.
13681, Springer Nature, 2022, pp. 350–65, doi:10.1007/978-3-031-19803-8_21.
short: B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature,
2022, pp. 350–365.
conference:
end_date: 2022-10-27
location: Tel Aviv, Israel
name: 'ECCV: European Conference on Computer Vision'
start_date: 2022-10-23
date_created: 2022-08-12T15:09:47Z
date_published: 2022-10-23T00:00:00Z
date_updated: 2023-05-03T08:00:46Z
day: '23'
department:
- _id: GradSch
- _id: ChLa
doi: 10.1007/978-3-031-19803-8_21
external_id:
arxiv:
- '2208.03160'
intvolume: ' 13681'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2208.03160'
month: '10'
oa: 1
oa_version: Preprint
page: 350-365
publication: Computer Vision – ECCV 2022
publication_identifier:
eisbn:
- '9783031198038'
isbn:
- '9783031198021'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Almost-orthogonal layers for efficient general-purpose Lipschitz networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13681
year: '2022'
...
---
_id: '10752'
abstract:
- lang: eng
text: 'The digitalization of almost all aspects of our everyday lives has led to
unprecedented amounts of data being freely available on the Internet. In particular
social media platforms provide rich sources of user-generated data, though typically
in unstructured form, and with high diversity, such as written in many different
languages. Automatically identifying meaningful information in such big data resources
and extracting it efficiently is one of the ongoing challenges of our time. A
common step for this is sentiment analysis, which forms the foundation for tasks
such as opinion mining or trend prediction. Unfortunately, publicly available
tools for this task are almost exclusively available for English-language texts.
Consequently, a large fraction of the Internet users, who do not communicate in
English, are ignored in automatized studies, a phenomenon called rare-language
discrimination.In this work we propose a technique to overcome this problem by
a truly multi-lingual model, which can be trained automatically without linguistic
knowledge or even the ability to read the many target languages. The main step
is to combine self-annotation, specifically the use of emoticons as a proxy for
labels, with multi-lingual sentence representations.To evaluate our method we
curated several large datasets from data obtained via the free Twitter streaming
API. The results show that our proposed multi-lingual training is able to achieve
sentiment predictions at the same quality level for rare languages as for frequent
ones, and in particular clearly better than what mono-lingual training achieves
on the same data. '
article_processing_charge: No
author:
- first_name: Jasmin
full_name: Lampert, Jasmin
last_name: Lampert
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0002-4561-241X
citation:
ama: 'Lampert J, Lampert C. Overcoming rare-language discrimination in multi-lingual
sentiment analysis. In: 2021 IEEE International Conference on Big Data.
IEEE; 2022:5185-5192. doi:10.1109/bigdata52589.2021.9672003'
apa: 'Lampert, J., & Lampert, C. (2022). Overcoming rare-language discrimination
in multi-lingual sentiment analysis. In 2021 IEEE International Conference
on Big Data (pp. 5185–5192). Orlando, FL, United States: IEEE. https://doi.org/10.1109/bigdata52589.2021.9672003'
chicago: Lampert, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination
in Multi-Lingual Sentiment Analysis.” In 2021 IEEE International Conference
on Big Data, 5185–92. IEEE, 2022. https://doi.org/10.1109/bigdata52589.2021.9672003.
ieee: J. Lampert and C. Lampert, “Overcoming rare-language discrimination in multi-lingual
sentiment analysis,” in 2021 IEEE International Conference on Big Data,
Orlando, FL, United States, 2022, pp. 5185–5192.
ista: 'Lampert J, Lampert C. 2022. Overcoming rare-language discrimination in multi-lingual
sentiment analysis. 2021 IEEE International Conference on Big Data. Big Data:
International Conference on Big Data, 5185–5192.'
mla: Lampert, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination
in Multi-Lingual Sentiment Analysis.” 2021 IEEE International Conference on
Big Data, IEEE, 2022, pp. 5185–92, doi:10.1109/bigdata52589.2021.9672003.
short: J. Lampert, C. Lampert, in:, 2021 IEEE International Conference on Big Data,
IEEE, 2022, pp. 5185–5192.
conference:
end_date: 2021-12-18
location: Orlando, FL, United States
name: 'Big Data: International Conference on Big Data'
start_date: 2021-12-15
date_created: 2022-02-10T14:08:23Z
date_published: 2022-01-13T00:00:00Z
date_updated: 2023-08-02T14:27:50Z
day: '13'
department:
- _id: ChLa
doi: 10.1109/bigdata52589.2021.9672003
external_id:
isi:
- '000800559505036'
isi: 1
language:
- iso: eng
month: '01'
oa_version: None
page: 5185-5192
publication: 2021 IEEE International Conference on Big Data
publication_identifier:
isbn:
- '9781665439022'
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Overcoming rare-language discrimination in multi-lingual sentiment analysis
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2022'
...
---
_id: '12161'
abstract:
- lang: eng
text: 'We introduce LIMES, a new method for learning with non-stationary streaming
data, inspired by the recent success of meta-learning. The main idea is not to
attempt to learn a single classifier that would have to work well across all occurring
data distributions, nor many separate classifiers, but to exploit a hybrid strategy:
we learn a single set of model parameters from which a specific classifier for
any specific data distribution is derived via classifier adaptation. Assuming
a multiclass classification setting with class-prior shift, the adaptation step
can be performed analytically with only the classifier’s bias terms being affected.
Another contribution of our work is an extrapolation step that predicts suitable
adaptation parameters for future time steps based on the previous data. In combination,
we obtain a lightweight procedure for learning from streaming data with varying
class distribution that adds no trainable parameters and almost no memory or computational
overhead compared to training a single model. Experiments on a set of exemplary
tasks using Twitter data show that LIMES achieves higher accuracy than alternative
approaches, especially with respect to the relevant real-world metric of lowest
within-day accuracy.'
article_processing_charge: No
author:
- first_name: Paulina
full_name: Tomaszewska, Paulina
last_name: Tomaszewska
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Tomaszewska P, Lampert C. Lightweight conditional model extrapolation for
streaming data under class-prior shift. In: 26th International Conference on
Pattern Recognition. Vol 2022. Institute of Electrical and Electronics Engineers;
2022:2128-2134. doi:10.1109/icpr56361.2022.9956195'
apa: 'Tomaszewska, P., & Lampert, C. (2022). Lightweight conditional model extrapolation
for streaming data under class-prior shift. In 26th International Conference
on Pattern Recognition (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/icpr56361.2022.9956195'
chicago: Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model
Extrapolation for Streaming Data under Class-Prior Shift.” In 26th International
Conference on Pattern Recognition, 2022:2128–34. Institute of Electrical and
Electronics Engineers, 2022. https://doi.org/10.1109/icpr56361.2022.9956195.
ieee: P. Tomaszewska and C. Lampert, “Lightweight conditional model extrapolation
for streaming data under class-prior shift,” in 26th International Conference
on Pattern Recognition, Montreal, Canada, 2022, vol. 2022, pp. 2128–2134.
ista: 'Tomaszewska P, Lampert C. 2022. Lightweight conditional model extrapolation
for streaming data under class-prior shift. 26th International Conference on Pattern
Recognition. ICPR: International Conference on Pattern Recognition vol. 2022,
2128–2134.'
mla: Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model
Extrapolation for Streaming Data under Class-Prior Shift.” 26th International
Conference on Pattern Recognition, vol. 2022, Institute of Electrical and
Electronics Engineers, 2022, pp. 2128–34, doi:10.1109/icpr56361.2022.9956195.
short: P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern
Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134.
conference:
end_date: 2022-08-25
location: Montreal, Canada
name: 'ICPR: International Conference on Pattern Recognition'
start_date: 2022-08-21
date_created: 2023-01-12T12:09:38Z
date_published: 2022-11-29T00:00:00Z
date_updated: 2023-08-04T09:06:34Z
day: '29'
department:
- _id: ChLa
doi: 10.1109/icpr56361.2022.9956195
external_id:
arxiv:
- '2206.05181'
isi:
- '000897707602018'
intvolume: ' 2022'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2206.05181
month: '11'
oa: 1
oa_version: Preprint
page: 2128-2134
publication: 26th International Conference on Pattern Recognition
publication_identifier:
eisbn:
- '9781665490627'
eissn:
- 2831-7475
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Lightweight conditional model extrapolation for streaming data under class-prior
shift
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 2022
year: '2022'
...
---
_id: '12299'
abstract:
- lang: eng
text: 'Transfer learning is a classic paradigm by which models pretrained on large
“upstream” datasets are adapted to yield good results on “downstream” specialized
datasets. Generally, more accurate models on the “upstream” dataset tend to provide
better transfer accuracy “downstream”. In this work, we perform an in-depth investigation
of this phenomenon in the context of convolutional neural networks (CNNs) trained
on the ImageNet dataset, which have been pruned-that is, compressed by sparsifiying
their connections. We consider transfer using unstructured pruned models obtained
by applying several state-of-the-art pruning methods, including magnitude-based,
second-order, regrowth, lottery-ticket, and regularization approaches, in the
context of twelve standard transfer tasks. In a nutshell, our study shows that
sparse models can match or even outperform the transfer performance of dense models,
even at high sparsities, and, while doing so, can lead to significant inference
and even training speedups. At the same time, we observe and analyze significant
differences in the behaviour of different pruning methods. The code is available
at: https://github.com/IST-DASLab/sparse-imagenet-transfer.'
acknowledgement: he authors would like to sincerely thank Christoph Lampert and Nir
Shavit for fruitful discussions during the development of this work, and Eldar Kurtic
for experimental support. EI was supported in part by the FWF DK VGSCO, grant agreement
number W1260-N35, while AP and DA acknowledge generous support by the ERC, via Starting
Grant 805223 ScaleML.
article_processing_charge: No
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Mark
full_name: Kurtz, Mark
last_name: Kurtz
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Iofinova EB, Peste E-A, Kurtz M, Alistarh D-A. How well do sparse ImageNet
models transfer? In: 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. Institute of Electrical and Electronics Engineers; 2022:12256-12266.
doi:10.1109/cvpr52688.2022.01195'
apa: 'Iofinova, E. B., Peste, E.-A., Kurtz, M., & Alistarh, D.-A. (2022). How
well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195'
chicago: Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian
Alistarh. “How Well Do Sparse ImageNet Models Transfer?” In 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical
and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01195.
ieee: E. B. Iofinova, E.-A. Peste, M. Kurtz, and D.-A. Alistarh, “How well do sparse
ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision
and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
ista: 'Iofinova EB, Peste E-A, Kurtz M, Alistarh D-A. 2022. How well do sparse ImageNet
models transfer? 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
CVPR: Computer Vision and Pattern Recognition, 12256–12266.'
mla: Iofinova, Eugenia B., et al. “How Well Do Sparse ImageNet Models Transfer?”
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute
of Electrical and Electronics Engineers, 2022, pp. 12256–66, doi:10.1109/cvpr52688.2022.01195.
short: E.B. Iofinova, E.-A. Peste, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Institute of Electrical
and Electronics Engineers, 2022, pp. 12256–12266.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2022-06-18
date_created: 2023-01-16T10:06:00Z
date_published: 2022-09-27T00:00:00Z
date_updated: 2023-08-04T10:33:28Z
day: '27'
department:
- _id: DaAl
- _id: ChLa
doi: 10.1109/cvpr52688.2022.01195
ec_funded: 1
external_id:
arxiv:
- '2111.13445'
isi:
- '000870759105034'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.13445
month: '09'
oa: 1
oa_version: Preprint
page: 12256-12266
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: ' W1260-N35'
name: Vienna Graduate School on Computational Optimization
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: How well do sparse ImageNet models transfer?
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2022'
...
---
_id: '10802'
abstract:
- lang: eng
text: "Addressing fairness concerns about machine learning models is a crucial step
towards their long-term adoption in real-world automated systems. While many approaches
have been developed for training fair models from data, little is known about
the robustness of these methods to data corruption. In this work we consider fairness-aware
learning under worst-case data manipulations. We show that an adversary can in
some situations force any learner to return an overly biased classifier, regardless
of the sample size and with or without degrading\r\naccuracy, and that the strength
of the excess bias increases for learning problems with underrepresented protected
groups in the data. We also prove that our hardness results are tight up to constant
factors. To this end, we study two natural learning algorithms that optimize for
both accuracy and fairness and show that these algorithms enjoy guarantees that
are order-optimal in terms of the corruption ratio and the protected groups frequencies
in the large data\r\nlimit."
acknowledgement: The authors thank Eugenia Iofinova and Bernd Prach for providing
feedback on early versions of this paper. This publication was made possible by
an ETH AI Center postdoctoral fellowship to Nikola Konstantinov.
article_processing_charge: No
article_type: original
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0002-4561-241X
citation:
ama: Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data.
Journal of Machine Learning Research. 2022;23:1-60.
apa: Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning
from corrupted data. Journal of Machine Learning Research. ML Research
Press.
chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness-Aware PAC Learning
from Corrupted Data.” Journal of Machine Learning Research. ML Research
Press, 2022.
ieee: N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted
data,” Journal of Machine Learning Research, vol. 23. ML Research Press,
pp. 1–60, 2022.
ista: Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted
data. Journal of Machine Learning Research. 23, 1–60.
mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning
from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML
Research Press, 2022, pp. 1–60.
short: N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022)
1–60.
date_created: 2022-02-28T14:05:42Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2023-09-26T10:44:37Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
external_id:
arxiv:
- '2102.06004'
file:
- access_level: open_access
checksum: 9cac897b54a0ddf3a553a2c33e88cfda
content_type: application/pdf
creator: kschuh
date_created: 2022-07-12T15:08:28Z
date_updated: 2022-07-12T15:08:28Z
file_id: '11570'
file_name: 2022_JournalMachineLearningResearch_Konstantinov.pdf
file_size: 551862
relation: main_file
success: 1
file_date_updated: 2022-07-12T15:08:28Z
has_accepted_license: '1'
intvolume: ' 23'
keyword:
- Fairness
- robustness
- data poisoning
- trustworthy machine learning
- PAC learning
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 1-60
publication: Journal of Machine Learning Research
publication_identifier:
eissn:
- 1533-7928
issn:
- 1532-4435
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '10799'
relation: dissertation_contains
status: public
- id: '13241'
relation: shorter_version
status: public
scopus_import: '1'
status: public
title: Fairness-aware PAC learning from corrupted data
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 23
year: '2022'
...
---
_id: '13241'
abstract:
- lang: eng
text: Addressing fairness concerns about machine learning models is a crucial step
towards their long-term adoption in real-world automated systems. Many approaches
for training fair models from data have been developed and an implicit assumption
about such algorithms is that they are able to recover a fair model, despite potential
historical biases in the data. In this work we show a number of impossibility
results that indicate that there is no learning algorithm that can recover a fair
model when a proportion of the dataset is subject to arbitrary manipulations.
Specifically, we prove that there are situations in which an adversary can force
any learner to return a biased classifier, with or without degrading accuracy,
and that the strength of this bias increases for learning problems with underrepresented
protected groups in the data. Our results emphasize on the importance of studying
further data corruption models of various strength and of establishing stricter
data collection practices for fairness-aware learning.
acknowledgement: "This paper is a shortened, workshop version of Konstantinov and
Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including
an analysis of algorithms achieving the lower bounds from this paper, we refer to
the full version."
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning
from corrupted data. In: Proceedings of Machine Learning Research. Vol
171. ML Research Press; 2022:59-83.'
apa: Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware
learning from corrupted data. In Proceedings of Machine Learning Research
(Vol. 171, pp. 59–83). ML Research Press.
chicago: Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of
Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning
Research, 171:59–83. ML Research Press, 2022.
ieee: N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware
learning from corrupted data,” in Proceedings of Machine Learning Research,
2022, vol. 171, pp. 59–83.
ista: Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning
from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.
mla: Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware
Learning from Corrupted Data.” Proceedings of Machine Learning Research,
vol. 171, ML Research Press, 2022, pp. 59–83.
short: N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research,
ML Research Press, 2022, pp. 59–83.
date_created: 2023-07-16T22:01:13Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2023-09-26T10:44:37Z
day: '01'
department:
- _id: ChLa
external_id:
arxiv:
- '2102.06004'
intvolume: ' 171'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2102.06004
month: '12'
oa: 1
oa_version: Preprint
page: 59-83
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
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status: public
title: On the impossibility of fairness-aware learning from corrupted data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 171
year: '2022'
...
---
_id: '10799'
abstract:
- lang: eng
text: "Because of the increasing popularity of machine learning methods, it is becoming
important to understand the impact of learned components on automated decision-making
systems and to guarantee that their consequences are beneficial to society. In
other words, it is necessary to ensure that machine learning is sufficiently trustworthy
to be used in real-world applications. This thesis studies two properties of machine
learning models that are highly desirable for the\r\nsake of reliability: robustness
and fairness. In the first part of the thesis we study the robustness of learning
algorithms to training data corruption. Previous work has shown that machine learning
models are vulnerable to a range\r\nof training set issues, varying from label
noise through systematic biases to worst-case data manipulations. This is an especially
relevant problem from a present perspective, since modern machine learning methods
are particularly data hungry and therefore practitioners often have to rely on
data collected from various external sources, e.g. from the Internet, from app
users or via crowdsourcing. Naturally, such sources vary greatly in the quality
and reliability of the\r\ndata they provide. With these considerations in mind,
we study the problem of designing machine learning algorithms that are robust
to corruptions in data coming from multiple sources. We show that, in contrast
to the case of a single dataset with outliers, successful learning within this
model is possible both theoretically and practically, even under worst-case data
corruptions. The second part of this thesis deals with fairness-aware machine
learning. There are multiple areas where machine learning models have shown promising
results, but where careful considerations are required, in order to avoid discrimanative
decisions taken by such learned components. Ensuring fairness can be particularly
challenging, because real-world training datasets are expected to contain various
forms of historical bias that may affect the learning process. In this thesis
we show that data corruption can indeed render the problem of achieving fairness
impossible, by tightly characterizing the theoretical limits of fair learning
under worst-case data manipulations. However, assuming access to clean data, we
also show how fairness-aware learning can be made practical in contexts beyond
binary classification, in particular in the challenging learning to rank setting."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
citation:
ama: Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799
apa: Konstantinov, N. H. (2022). Robustness and fairness in machine learning.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
chicago: Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.”
Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10799.
ieee: N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute
of Science and Technology Austria, 2022.
ista: Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute
of Science and Technology Austria.
mla: Konstantinov, Nikola H. Robustness and Fairness in Machine Learning.
Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.
short: N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute
of Science and Technology Austria, 2022.
date_created: 2022-02-28T13:03:49Z
date_published: 2022-03-08T00:00:00Z
date_updated: 2023-10-17T12:31:54Z
day: '08'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChLa
doi: 10.15479/at:ista:10799
ec_funded: 1
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file_size: 22841103
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file_date_updated: 2022-03-10T12:11:48Z
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keyword:
- robustness
- fairness
- machine learning
- PAC learning
- adversarial learning
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '176'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
isbn:
- 978-3-99078-015-2
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
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status: public
- id: '10803'
relation: part_of_dissertation
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- id: '10802'
relation: part_of_dissertation
status: public
- id: '6590'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Robustness and fairness in machine learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2022'
...
---
_id: '9210'
abstract:
- lang: eng
text: "Modern neural networks can easily fit their training set perfectly. Surprisingly,
despite being “overfit” in this way, they tend to generalize well to future data,
thereby defying the classic bias–variance trade-off of machine learning theory.
Of the many possible explanations, a prevalent one is that training by stochastic
gradient descent (SGD) imposes an implicit bias that leads it to learn simple
functions, and these simple functions generalize well. However, the specifics
of this implicit bias are not well understood.\r\nIn this work, we explore the
smoothness conjecture which states that SGD is implicitly biased towards learning
functions that are smooth. We propose several measures to formalize the intuitive
notion of smoothness, and we conduct experiments to determine whether SGD indeed
implicitly optimizes for these measures. Our findings rule out the possibility
that smoothness measures based on first-order derivatives are being implicitly
enforced. They are supportive, though, of the smoothness conjecture for measures
based on second-order derivatives."
article_processing_charge: No
author:
- first_name: Vaclav
full_name: Volhejn, Vaclav
id: d5235fb4-7a6d-11eb-b254-f25d12d631a8
last_name: Volhejn
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Volhejn V, Lampert C. Does SGD implicitly optimize for smoothness? In: 42nd
German Conference on Pattern Recognition. Vol 12544. LNCS. Springer; 2021:246-259.
doi:10.1007/978-3-030-71278-5_18'
apa: 'Volhejn, V., & Lampert, C. (2021). Does SGD implicitly optimize for smoothness?
In 42nd German Conference on Pattern Recognition (Vol. 12544, pp. 246–259).
Tübingen, Germany: Springer. https://doi.org/10.1007/978-3-030-71278-5_18'
chicago: Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for
Smoothness?” In 42nd German Conference on Pattern Recognition, 12544:246–59.
LNCS. Springer, 2021. https://doi.org/10.1007/978-3-030-71278-5_18.
ieee: V. Volhejn and C. Lampert, “Does SGD implicitly optimize for smoothness?,”
in 42nd German Conference on Pattern Recognition, Tübingen, Germany, 2021,
vol. 12544, pp. 246–259.
ista: 'Volhejn V, Lampert C. 2021. Does SGD implicitly optimize for smoothness?
42nd German Conference on Pattern Recognition. DAGM GCPR: German Conference on
Pattern Recognition LNCS vol. 12544, 246–259.'
mla: Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?”
42nd German Conference on Pattern Recognition, vol. 12544, Springer, 2021,
pp. 246–59, doi:10.1007/978-3-030-71278-5_18.
short: V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition,
Springer, 2021, pp. 246–259.
conference:
end_date: 2020-10-01
location: Tübingen, Germany
name: 'DAGM GCPR: German Conference on Pattern Recognition '
start_date: 2020-09-28
date_created: 2021-03-01T09:01:16Z
date_published: 2021-03-17T00:00:00Z
date_updated: 2022-08-12T07:28:47Z
day: '17'
ddc:
- '510'
department:
- _id: ChLa
doi: 10.1007/978-3-030-71278-5_18
file:
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checksum: 3e3628ab1cf658d82524963f808004ea
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success: 1
file_date_updated: 2022-08-12T07:27:58Z
has_accepted_license: '1'
intvolume: ' 12544'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Submitted Version
page: 246-259
publication: 42nd German Conference on Pattern Recognition
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030712778'
issn:
- 0302-9743
publication_status: published
publisher: Springer
quality_controlled: '1'
scopus_import: '1'
series_title: LNCS
status: public
title: Does SGD implicitly optimize for smoothness?
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 12544
year: '2021'
...
---
_id: '9416'
abstract:
- lang: eng
text: 'We study the inductive bias of two-layer ReLU networks trained by gradient
flow. We identify a class of easy-to-learn (`orthogonally separable'') datasets,
and characterise the solution that ReLU networks trained on such datasets converge
to. Irrespective of network width, the solution turns out to be a combination
of two max-margin classifiers: one corresponding to the positive data subset and
one corresponding to the negative data subset. The proof is based on the recently
introduced concept of extremal sectors, for which we prove a number of properties
in the context of orthogonal separability. In particular, we prove stationarity
of activation patterns from some time onwards, which enables a reduction of the
ReLU network to an ensemble of linear subnetworks.'
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. The inductive bias of ReLU networks on orthogonally separable
data. In: 9th International Conference on Learning Representations. ; 2021.'
apa: Phuong, M., & Lampert, C. (2021). The inductive bias of ReLU networks on
orthogonally separable data. In 9th International Conference on Learning Representations.
Virtual.
chicago: Phuong, Mary, and Christoph Lampert. “The Inductive Bias of ReLU Networks
on Orthogonally Separable Data.” In 9th International Conference on Learning
Representations, 2021.
ieee: M. Phuong and C. Lampert, “The inductive bias of ReLU networks on orthogonally
separable data,” in 9th International Conference on Learning Representations,
Virtual, 2021.
ista: 'Phuong M, Lampert C. 2021. The inductive bias of ReLU networks on orthogonally
separable data. 9th International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Phuong, Mary, and Christoph Lampert. “The Inductive Bias of ReLU Networks on
Orthogonally Separable Data.” 9th International Conference on Learning Representations,
2021.
short: M. Phuong, C. Lampert, in:, 9th International Conference on Learning Representations,
2021.
conference:
end_date: 2021-05-07
location: Virtual
name: ' ICLR: International Conference on Learning Representations'
start_date: 2021-05-03
date_created: 2021-05-24T11:16:46Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-09-07T13:29:50Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ChLa
file:
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checksum: f34ff17017527db5ba6927f817bdd125
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creator: bphuong
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file_id: '9417'
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language:
- iso: eng
main_file_link:
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month: '05'
oa: 1
oa_version: Published Version
publication: 9th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
related_material:
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relation: dissertation_contains
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scopus_import: '1'
status: public
title: The inductive bias of ReLU networks on orthogonally separable data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10803'
abstract:
- lang: eng
text: Given the abundance of applications of ranking in recent years, addressing
fairness concerns around automated ranking systems becomes necessary for increasing
the trust among end-users. Previous work on fair ranking has mostly focused on
application-specific fairness notions, often tailored to online advertising, and
it rarely considers learning as part of the process. In this work, we show how
to transfer numerous fairness notions from binary classification to a learning
to rank setting. Our formalism allows us to design methods for incorporating fairness
objectives with provable generalization guarantees. An extensive experimental
evaluation shows that our method can improve ranking fairness substantially with
no or only little loss of model quality.
article_number: '2102.05996'
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0002-4561-241X
citation:
ama: Konstantinov NH, Lampert C. Fairness through regularization for learning to
rank. arXiv. doi:10.48550/arXiv.2102.05996
apa: Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization
for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996
chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization
for Learning to Rank.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2102.05996.
ieee: N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning
to rank,” arXiv. .
ista: Konstantinov NH, Lampert C. Fairness through regularization for learning to
rank. arXiv, 2102.05996.
mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization
for Learning to Rank.” ArXiv, 2102.05996, doi:10.48550/arXiv.2102.05996.
short: N.H. Konstantinov, C. Lampert, ArXiv (n.d.).
date_created: 2022-02-28T14:13:59Z
date_published: 2021-06-07T00:00:00Z
date_updated: 2023-09-07T13:42:08Z
day: '07'
department:
- _id: ChLa
doi: 10.48550/arXiv.2102.05996
external_id:
arxiv:
- '2102.05996'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2102.05996
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
related_material:
record:
- id: '10799'
relation: dissertation_contains
status: public
status: public
title: Fairness through regularization for learning to rank
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9418'
abstract:
- lang: eng
text: "Deep learning is best known for its empirical success across a wide range
of applications\r\nspanning computer vision, natural language processing and speech.
Of equal significance,\r\nthough perhaps less known, are its ramifications for
learning theory: deep networks have\r\nbeen observed to perform surprisingly well
in the high-capacity regime, aka the overfitting\r\nor underspecified regime.
Classically, this regime on the far right of the bias-variance curve\r\nis associated
with poor generalisation; however, recent experiments with deep networks\r\nchallenge
this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification
in deep learning.\r\nFirst, we argue that deep learning models are underspecified
on two levels: a) any given\r\ntraining dataset can be fit by many different functions,
and b) any given function can be\r\nexpressed by many different parameter configurations.
We refer to the second kind of\r\nunderspecification as parameterisation redundancy
and we precisely characterise its extent.\r\nSecond, we characterise the implicit
criteria (the inductive bias) that guide learning in the\r\nunderspecified regime.
Specifically, we consider a nonlinear but tractable classification\r\nsetting,
and show that given the choice, neural networks learn classifiers with a large
margin.\r\nThird, we consider learning scenarios where the inductive bias is not
by itself sufficient to\r\ndeal with underspecification. We then study different
ways of ‘tightening the specification’: i)\r\nIn the setting of representation
learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser
based on mutual information. ii) In the setting of binary classification, we\r\nconsider
soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks
supervised in this way and verify that soft labels facilitate fast learning. Finally,
we\r\nexplore an application of soft-label supervision to the training of multi-exit
models."
acknowledged_ssus:
- _id: ScienComp
- _id: CampIT
- _id: E-Lib
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
citation:
ama: Phuong M. Underspecification in deep learning. 2021. doi:10.15479/AT:ISTA:9418
apa: Phuong, M. (2021). Underspecification in deep learning. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9418
chicago: Phuong, Mary. “Underspecification in Deep Learning.” Institute of Science
and Technology Austria, 2021. https://doi.org/10.15479/AT:ISTA:9418.
ieee: M. Phuong, “Underspecification in deep learning,” Institute of Science and
Technology Austria, 2021.
ista: Phuong M. 2021. Underspecification in deep learning. Institute of Science
and Technology Austria.
mla: Phuong, Mary. Underspecification in Deep Learning. Institute of Science
and Technology Austria, 2021, doi:10.15479/AT:ISTA:9418.
short: M. Phuong, Underspecification in Deep Learning, Institute of Science and
Technology Austria, 2021.
date_created: 2021-05-24T13:06:23Z
date_published: 2021-05-30T00:00:00Z
date_updated: 2023-09-08T11:11:12Z
day: '30'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChLa
doi: 10.15479/AT:ISTA:9418
file:
- access_level: open_access
checksum: 4f0abe64114cfed264f9d36e8d1197e3
content_type: application/pdf
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language:
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month: '05'
oa: 1
oa_version: Published Version
page: '125'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7435'
relation: part_of_dissertation
status: deleted
- id: '7481'
relation: part_of_dissertation
status: public
- id: '9416'
relation: part_of_dissertation
status: public
- id: '7479'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Underspecification in deep learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2021'
...
---
_id: '14987'
abstract:
- lang: eng
text: "The goal of zero-shot learning is to construct a classifier that can identify
object classes for which no training examples are available. When training data
for some of the object classes is available but not for others, the name generalized
zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot
is also used to describe other machine learning-based approaches that require
no training data from the problem of interest, such as zero-shot action recognition
or zero-shot machine translation."
article_processing_charge: No
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision.
2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874'
apa: 'Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision
(2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874'
chicago: 'Lampert, Christoph. “Zero-Shot Learning.” In Computer Vision, edited
by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63416-2_874.'
ieee: 'C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K.
Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.'
ista: 'Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.'
mla: Lampert, Christoph. “Zero-Shot Learning.” Computer Vision, edited by
Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874.
short: C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham,
2021, pp. 1395–1397.
date_created: 2024-02-14T14:05:32Z
date_published: 2021-10-13T00:00:00Z
date_updated: 2024-02-19T10:59:04Z
day: '13'
department:
- _id: ChLa
doi: 10.1007/978-3-030-63416-2_874
edition: '2'
editor:
- first_name: Katsushi
full_name: Ikeuchi, Katsushi
last_name: Ikeuchi
language:
- iso: eng
month: '10'
oa_version: None
page: 1395-1397
place: Cham
publication: Computer Vision
publication_identifier:
eisbn:
- '9783030634162'
isbn:
- '9783030634155'
publication_status: published
publisher: Springer
quality_controlled: '1'
status: public
title: Zero-Shot Learning
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '8063'
abstract:
- lang: eng
text: "We present a generative model of images that explicitly reasons over the
set\r\nof objects they show. Our model learns a structured latent representation
that\r\nseparates objects from each other and from the background; unlike prior
works,\r\nit explicitly represents the 2D position and depth of each object, as
well as\r\nan embedding of its segmentation mask and appearance. The model can
be trained\r\nfrom images alone in a purely unsupervised fashion without the need
for object\r\nmasks or depth information. Moreover, it always generates complete
objects,\r\neven though a significant fraction of training images contain occlusions.\r\nFinally,
we show that our model can infer decompositions of novel images into\r\ntheir
constituent objects, including accurate prediction of depth ordering and\r\nsegmentation
of occluded parts."
article_number: '2004.00642'
article_processing_charge: No
author:
- first_name: Titas
full_name: Anciukevicius, Titas
last_name: Anciukevicius
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
citation:
ama: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with
factored depths, locations, and appearances. arXiv.
apa: Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric
image generation with factored depths, locations, and appearances. arXiv.
chicago: Anciukevicius, Titas, Christoph Lampert, and Paul M Henderson. “Object-Centric
Image Generation with Factored Depths, Locations, and Appearances.” ArXiv,
n.d.
ieee: T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation
with factored depths, locations, and appearances,” arXiv. .
ista: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation
with factored depths, locations, and appearances. arXiv, 2004.00642.
mla: Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored
Depths, Locations, and Appearances.” ArXiv, 2004.00642.
short: T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.).
date_created: 2020-06-29T23:55:23Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2021-01-12T08:16:44Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
external_id:
arxiv:
- '2004.00642'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2004.00642
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Object-centric image generation with factored depths, locations, and appearances
tmp:
image: /images/cc_by_sa.png
legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode
name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC
BY-SA 4.0)
short: CC BY-SA (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8188'
abstract:
- lang: eng
text: "A natural approach to generative modeling of videos is to represent them
as a composition of moving objects. Recent works model a set of 2D sprites over
a slowly-varying background, but without considering the underlying 3D scene that\r\ngives
rise to them. We instead propose to model a video as the view seen while moving
through a scene with multiple 3D objects and a 3D background. Our model is trained
from monocular videos without any supervision, yet learns to\r\ngenerate coherent
3D scenes containing several moving objects. We conduct detailed experiments on
two datasets, going beyond the visual complexity supported by state-of-the-art
generative approaches. We evaluate our method on\r\ndepth-prediction and 3D object
detection---tasks which cannot be addressed by those earlier works---and show
it out-performs them even on 2D instance segmentation and tracking."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This research was supported by the Scientific Service Units (SSU)
of IST Austria through resources\r\nprovided by Scientific Computing (SciComp).
PH is employed part-time by Blackford Analysis, but\r\nthey did not support this
project in any way."
article_processing_charge: No
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Henderson PM, Lampert C. Unsupervised object-centric video generation and
decomposition in 3D. In: 34th Conference on Neural Information Processing Systems.
Vol 33. Curran Associates; 2020:3106–3117.'
apa: 'Henderson, P. M., & Lampert, C. (2020). Unsupervised object-centric video
generation and decomposition in 3D. In 34th Conference on Neural Information
Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.'
chicago: Henderson, Paul M, and Christoph Lampert. “Unsupervised Object-Centric
Video Generation and Decomposition in 3D.” In 34th Conference on Neural Information
Processing Systems, 33:3106–3117. Curran Associates, 2020.
ieee: P. M. Henderson and C. Lampert, “Unsupervised object-centric video generation
and decomposition in 3D,” in 34th Conference on Neural Information Processing
Systems, Vancouver, Canada, 2020, vol. 33, pp. 3106–3117.
ista: 'Henderson PM, Lampert C. 2020. Unsupervised object-centric video generation
and decomposition in 3D. 34th Conference on Neural Information Processing Systems.
NeurIPS: Neural Information Processing Systems vol. 33, 3106–3117.'
mla: Henderson, Paul M., and Christoph Lampert. “Unsupervised Object-Centric Video
Generation and Decomposition in 3D.” 34th Conference on Neural Information
Processing Systems, vol. 33, Curran Associates, 2020, pp. 3106–3117.
short: P.M. Henderson, C. Lampert, in:, 34th Conference on Neural Information Processing
Systems, Curran Associates, 2020, pp. 3106–3117.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2020-07-31T16:59:19Z
date_published: 2020-07-07T00:00:00Z
date_updated: 2023-04-25T09:49:58Z
day: '07'
department:
- _id: ChLa
external_id:
arxiv:
- '2007.06705'
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2007.06705
month: '07'
oa: 1
oa_version: Preprint
page: 3106–3117
publication: 34th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713829546'
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Unsupervised object-centric video generation and decomposition in 3D
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 33
year: '2020'
...
---
_id: '6952'
abstract:
- lang: eng
text: 'We present a unified framework tackling two problems: class-specific 3D reconstruction
from a single image, and generation of new 3D shape samples. These tasks have
received considerable attention recently; however, most existing approaches rely
on 3D supervision, annotation of 2D images with keypoints or poses, and/or training
with multiple views of each object instance. Our framework is very general: it
can be trained in similar settings to existing approaches, while also supporting
weaker supervision. Importantly, it can be trained purely from 2D images, without
pose annotations, and with only a single view per instance. We employ meshes as
an output representation, instead of voxels used in most prior work. This allows
us to reason over lighting parameters and exploit shading information during training,
which previous 2D-supervised methods cannot. Thus, our method can learn to generate
and reconstruct concave object classes. We evaluate our approach in various settings,
showing that: (i) it learns to disentangle shape from pose and lighting; (ii)
using shading in the loss improves performance compared to just silhouettes; (iii)
when using a standard single white light, our model outperforms state-of-the-art
2D-supervised methods, both with and without pose supervision, thanks to exploiting
shading cues; (iv) performance improves further when using multiple coloured lights,
even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced
by our model capture smooth surfaces and fine details better than voxel-based
approaches; and (vi) our approach supports concave classes such as bathtubs and
sofas, which methods based on silhouettes cannot learn.'
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: Henderson PM, Ferrari V. Learning single-image 3D reconstruction by generative
modelling of shape, pose and shading. International Journal of Computer Vision.
2020;128:835-854. doi:10.1007/s11263-019-01219-8
apa: Henderson, P. M., & Ferrari, V. (2020). Learning single-image 3D reconstruction
by generative modelling of shape, pose and shading. International Journal of
Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01219-8
chicago: Henderson, Paul M, and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction
by Generative Modelling of Shape, Pose and Shading.” International Journal
of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01219-8.
ieee: P. M. Henderson and V. Ferrari, “Learning single-image 3D reconstruction by
generative modelling of shape, pose and shading,” International Journal of
Computer Vision, vol. 128. Springer Nature, pp. 835–854, 2020.
ista: Henderson PM, Ferrari V. 2020. Learning single-image 3D reconstruction by
generative modelling of shape, pose and shading. International Journal of Computer
Vision. 128, 835–854.
mla: Henderson, Paul M., and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction
by Generative Modelling of Shape, Pose and Shading.” International Journal
of Computer Vision, vol. 128, Springer Nature, 2020, pp. 835–54, doi:10.1007/s11263-019-01219-8.
short: P.M. Henderson, V. Ferrari, International Journal of Computer Vision 128
(2020) 835–854.
date_created: 2019-10-17T13:38:20Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2023-08-17T14:01:16Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s11263-019-01219-8
external_id:
arxiv:
- '1901.06447'
isi:
- '000491042100002'
file:
- access_level: open_access
checksum: a0f05dd4f5f64e4f713d8d9d4b5b1e3f
content_type: application/pdf
creator: dernst
date_created: 2019-10-25T10:28:29Z
date_updated: 2020-07-14T12:47:46Z
file_id: '6973'
file_name: 2019_CompVision_Henderson.pdf
file_size: 2243134
relation: main_file
file_date_updated: 2020-07-14T12:47:46Z
has_accepted_license: '1'
intvolume: ' 128'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 835-854
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning single-image 3D reconstruction by generative modelling of shape, pose
and shading
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 128
year: '2020'
...
---
_id: '7936'
abstract:
- lang: eng
text: 'State-of-the-art detection systems are generally evaluated on their ability
to exhaustively retrieve objects densely distributed in the image, across a wide
variety of appearances and semantic categories. Orthogonal to this, many real-life
object detection applications, for example in remote sensing, instead require
dealing with large images that contain only a few small objects of a single class,
scattered heterogeneously across the space. In addition, they are often subject
to strict computational constraints, such as limited battery capacity and computing
power.To tackle these more practical scenarios, we propose a novel flexible detection
scheme that efficiently adapts to variable object sizes and densities: We rely
on a sequence of detection stages, each of which has the ability to predict groups
of objects as well as individuals. Similar to a detection cascade, this multi-stage
architecture spares computational effort by discarding large irrelevant regions
of the image early during the detection process. The ability to group objects
provides further computational and memory savings, as it allows working with lower
image resolutions in early stages, where groups are more easily detected than
individuals, as they are more salient. We report experimental results on two aerial
image datasets, and show that the proposed method is as accurate yet computationally
more efficient than standard single-shot detectors, consistently across three
different backbone architectures.'
article_number: 1716-1725
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. Localizing grouped instances for efficient detection in
low-resource scenarios. In: IEEE Winter Conference on Applications of Computer
Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288'
apa: 'Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient
detection in low-resource scenarios. In IEEE Winter Conference on Applications
of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288'
chicago: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for
Efficient Detection in Low-Resource Scenarios.” In IEEE Winter Conference on
Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093288.
ieee: A. Royer and C. Lampert, “Localizing grouped instances for efficient detection
in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer
Vision, Snowmass Village, CO, United States, 2020.
ista: 'Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection
in low-resource scenarios. IEEE Winter Conference on Applications of Computer
Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.'
mla: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient
Detection in Low-Resource Scenarios.” IEEE Winter Conference on Applications
of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288.
short: A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer
Vision, IEEE, 2020.
conference:
end_date: 2020-03-05
location: ' Snowmass Village, CO, United States'
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-09-07T13:16:17Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093288
external_id:
arxiv:
- '2004.12623'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2004.12623
month: '03'
oa: 1
oa_version: Preprint
publication: IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
isbn:
- '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Localizing grouped instances for efficient detection in low-resource scenarios
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7937'
abstract:
- lang: eng
text: 'Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained
convolutional network for a new visual recognition task. However, the orthogonal
setting of transferring knowledge from a pretrained network to a visually different
yet semantically close source is rarely considered: This commonly happens with
real-life data, which is not necessarily as clean as the training source (noise,
geometric transformations, different modalities, etc.).To tackle such scenarios,
we introduce a new, generalized form of fine-tuning, called flex-tuning, in which
any individual unit (e.g. layer) of a network can be tuned, and the most promising
one is chosen automatically. In order to make the method appealing for practical
use, we propose two lightweight and faster selection procedures that prove to
be good approximations in practice. We study these selection criteria empirically
across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning
individual units, despite its simplicity, yields very good results as an adaptation
technique. As it turns out, in contrast to common practice, rather than the last
fully-connected unit it is best to tune an intermediate or early one in many domain-
shift scenarios, which is accurately detected by flex-tuning.'
article_number: 2180-2189
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer
learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision.
IEEE; 2020. doi:10.1109/WACV45572.2020.9093635'
apa: 'Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort
transfer learning. In 2020 IEEE Winter Conference on Applications of Computer
Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635'
chicago: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for
Minimum-Effort Transfer Learning.” In 2020 IEEE Winter Conference on Applications
of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093635.
ieee: A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer
learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision,
Snowmass Village, CO, United States, 2020.
ista: 'Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort
transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision.
WACV: Winter Conference on Applications of Computer Vision, 2180–2189.'
mla: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort
Transfer Learning.” 2020 IEEE Winter Conference on Applications of Computer
Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635.
short: A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of
Computer Vision, IEEE, 2020.
conference:
end_date: 2020-03-05
location: Snowmass Village, CO, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-09-07T13:16:17Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093635
external_id:
arxiv:
- '2008.11995'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/2008.11995
month: '03'
oa: 1
oa_version: Preprint
publication: 2020 IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
isbn:
- '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A flexible selection scheme for minimum-effort transfer learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8092'
abstract:
- lang: eng
text: Image translation refers to the task of mapping images from a visual domain
to another. Given two unpaired collections of images, we aim to learn a mapping
between the corpus-level style of each collection, while preserving semantic content
shared across the two domains. We introduce xgan, a dual adversarial auto-encoder,
which captures a shared representation of the common domain semantic content in
an unsupervised way, while jointly learning the domain-to-domain image translations
in both directions. We exploit ideas from the domain adaptation literature and
define a semantic consistency loss which encourages the learned embedding to preserve
semantics shared across domains. We report promising qualitative results for the
task of face-to-cartoon translation. The cartoon dataset we collected for this
purpose, “CartoonSet”, is also publicly available as a new benchmark for semantic
style transfer at https://google.github.io/cartoonset/index.html.
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Konstantinos
full_name: Bousmalis, Konstantinos
last_name: Bousmalis
- first_name: Stephan
full_name: Gouws, Stephan
last_name: Gouws
- first_name: Fred
full_name: Bertsch, Fred
last_name: Bertsch
- first_name: Inbar
full_name: Mosseri, Inbar
last_name: Mosseri
- first_name: Forrester
full_name: Cole, Forrester
last_name: Cole
- first_name: Kevin
full_name: Murphy, Kevin
last_name: Murphy
citation:
ama: 'Royer A, Bousmalis K, Gouws S, et al. XGAN: Unsupervised image-to-image translation
for many-to-many mappings. In: Singh R, Vatsa M, Patel VM, Ratha N, eds. Domain
Adaptation for Visual Understanding. Springer Nature; 2020:33-49. doi:10.1007/978-3-030-30671-7_3'
apa: 'Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Mosseri, I., Cole, F., &
Murphy, K. (2020). XGAN: Unsupervised image-to-image translation for many-to-many
mappings. In R. Singh, M. Vatsa, V. M. Patel, & N. Ratha (Eds.), Domain
Adaptation for Visual Understanding (pp. 33–49). Springer Nature. https://doi.org/10.1007/978-3-030-30671-7_3'
chicago: 'Royer, Amélie, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar
Mosseri, Forrester Cole, and Kevin Murphy. “XGAN: Unsupervised Image-to-Image
Translation for Many-to-Many Mappings.” In Domain Adaptation for Visual Understanding,
edited by Richa Singh, Mayank Vatsa, Vishal M. Patel, and Nalini Ratha, 33–49.
Springer Nature, 2020. https://doi.org/10.1007/978-3-030-30671-7_3.'
ieee: 'A. Royer et al., “XGAN: Unsupervised image-to-image translation for
many-to-many mappings,” in Domain Adaptation for Visual Understanding,
R. Singh, M. Vatsa, V. M. Patel, and N. Ratha, Eds. Springer Nature, 2020, pp.
33–49.'
ista: 'Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K. 2020.XGAN:
Unsupervised image-to-image translation for many-to-many mappings. In: Domain
Adaptation for Visual Understanding. , 33–49.'
mla: 'Royer, Amélie, et al. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many
Mappings.” Domain Adaptation for Visual Understanding, edited by Richa
Singh et al., Springer Nature, 2020, pp. 33–49, doi:10.1007/978-3-030-30671-7_3.'
short: A. Royer, K. Bousmalis, S. Gouws, F. Bertsch, I. Mosseri, F. Cole, K. Murphy,
in:, R. Singh, M. Vatsa, V.M. Patel, N. Ratha (Eds.), Domain Adaptation for Visual
Understanding, Springer Nature, 2020, pp. 33–49.
date_created: 2020-07-05T22:00:46Z
date_published: 2020-01-08T00:00:00Z
date_updated: 2023-09-07T13:16:18Z
day: '08'
department:
- _id: ChLa
doi: 10.1007/978-3-030-30671-7_3
editor:
- first_name: Richa
full_name: Singh, Richa
last_name: Singh
- first_name: Mayank
full_name: Vatsa, Mayank
last_name: Vatsa
- first_name: Vishal M.
full_name: Patel, Vishal M.
last_name: Patel
- first_name: Nalini
full_name: Ratha, Nalini
last_name: Ratha
external_id:
arxiv:
- '1711.05139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1711.05139
month: '01'
oa: 1
oa_version: Preprint
page: 33-49
publication: Domain Adaptation for Visual Understanding
publication_identifier:
isbn:
- '9783030306717'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'XGAN: Unsupervised image-to-image translation for many-to-many mappings'
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7481'
abstract:
- lang: eng
text: 'We address the following question: How redundant is the parameterisation
of ReLU networks? Specifically, we consider transformations of the weight space
which leave the function implemented by the network intact. Two such transformations
are known for feed-forward architectures: permutation of neurons within a layer,
and positive scaling of all incoming weights of a neuron coupled with inverse
scaling of its outgoing weights. In this work, we show for architectures with
non-increasing widths that permutation and scaling are in fact the only function-preserving
weight transformations. For any eligible architecture we give an explicit construction
of a neural network such that any other network that implements the same function
can be obtained from the original one by the application of permutations and rescaling. The
proof relies on a geometric understanding of boundaries between linear regions
of ReLU networks, and we hope the developed mathematical tools are of independent
interest.'
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. Functional vs. parametric equivalence of ReLU networks.
In: 8th International Conference on Learning Representations. ; 2020.'
apa: Phuong, M., & Lampert, C. (2020). Functional vs. parametric equivalence
of ReLU networks. In 8th International Conference on Learning Representations.
Online.
chicago: Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence
of ReLU Networks.” In 8th International Conference on Learning Representations,
2020.
ieee: M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,”
in 8th International Conference on Learning Representations, Online, 2020.
ista: 'Phuong M, Lampert C. 2020. Functional vs. parametric equivalence of ReLU
networks. 8th International Conference on Learning Representations. ICLR: International
Conference on Learning Representations.'
mla: Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence
of ReLU Networks.” 8th International Conference on Learning Representations,
2020.
short: M. Phuong, C. Lampert, in:, 8th International Conference on Learning Representations,
2020.
conference:
end_date: 2020-04-30
location: Online
name: 'ICLR: International Conference on Learning Representations'
start_date: 2020-04-27
date_created: 2020-02-11T09:07:37Z
date_published: 2020-04-26T00:00:00Z
date_updated: 2023-09-07T13:29:50Z
day: '26'
ddc:
- '000'
department:
- _id: ChLa
file:
- access_level: open_access
checksum: 8d372ea5defd8cb8fdc430111ed754a9
content_type: application/pdf
creator: bphuong
date_created: 2020-02-11T09:07:27Z
date_updated: 2020-07-14T12:47:59Z
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file_size: 405469
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file_date_updated: 2020-07-14T12:47:59Z
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language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
publication: 8th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: supplementary_material
url: https://iclr.cc/virtual_2020/poster_Bylx-TNKvH.html
record:
- id: '9418'
relation: dissertation_contains
status: public
status: public
title: Functional vs. parametric equivalence of ReLU networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8724'
abstract:
- lang: eng
text: "We study the problem of learning from multiple untrusted data sources, a
scenario of increasing practical relevance given the recent emergence of crowdsourcing
and collaborative learning paradigms. Specifically, we analyze the situation in
which a learning system obtains datasets from multiple sources, some of which
might be biased or even adversarially perturbed. It is\r\nknown that in the single-source
case, an adversary with the power to corrupt a fixed fraction of the training
data can prevent PAC-learnability, that is, even in the limit of infinitely much
training data, no learning system can approach the optimal test error. In this
work we show that, surprisingly, the same is not true in the multi-source setting,
where the adversary can arbitrarily\r\ncorrupt a fixed fraction of the data sources.
Our main results are a generalization bound that provides finite-sample guarantees
for this learning setting, as well as corresponding lower bounds. Besides establishing
PAC-learnability our results also show that in a cooperative learning setting
sharing data with other parties has provable benefits, even if some\r\nparticipants
are malicious. "
acknowledged_ssus:
- _id: ScienComp
acknowledgement: Dan Alistarh is supported in part by the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 805223 ScaleML). This research was supported by the Scientific
Service Units (SSU) of IST Austria through resources provided by Scientific Computing
(SciComp).
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity
of adversarial multi-source PAC learning. In: Proceedings of the 37th International
Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.'
apa: 'Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020).
On the sample complexity of adversarial multi-source PAC learning. In Proceedings
of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425).
Online: ML Research Press.'
chicago: Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph
Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.”
In Proceedings of the 37th International Conference on Machine Learning,
119:5416–25. ML Research Press, 2020.
ieee: N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample
complexity of adversarial multi-source PAC learning,” in Proceedings of the
37th International Conference on Machine Learning, Online, 2020, vol. 119,
pp. 5416–5425.
ista: 'Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample
complexity of adversarial multi-source PAC learning. Proceedings of the 37th International
Conference on Machine Learning. ICML: International Conference on Machine Learning
vol. 119, 5416–5425.'
mla: Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source
PAC Learning.” Proceedings of the 37th International Conference on Machine
Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.
short: N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings
of the 37th International Conference on Machine Learning, ML Research Press, 2020,
pp. 5416–5425.
conference:
end_date: 2020-07-18
location: Online
name: 'ICML: International Conference on Machine Learning'
start_date: 2020-07-12
date_created: 2020-11-05T15:25:58Z
date_published: 2020-07-12T00:00:00Z
date_updated: 2023-09-07T13:42:08Z
day: '12'
ddc:
- '000'
department:
- _id: DaAl
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '2002.10384'
file:
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checksum: cc755d0054bc4b2be778ea7aa7884d2f
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creator: dernst
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file_name: 2020_PMLR_Konstantinov.pdf
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success: 1
file_date_updated: 2021-02-15T09:00:01Z
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intvolume: ' 119'
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oa: 1
oa_version: Published Version
page: 5416-5425
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 37th International Conference on Machine Learning
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: supplementary_material
url: http://proceedings.mlr.press/v119/konstantinov20a/konstantinov20a-supp.pdf
record:
- id: '10799'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: On the sample complexity of adversarial multi-source PAC learning
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 119
year: '2020'
...
---
_id: '8390'
abstract:
- lang: eng
text: "Deep neural networks have established a new standard for data-dependent feature
extraction pipelines in the Computer Vision literature. Despite their remarkable
performance in the standard supervised learning scenario, i.e. when models are
trained with labeled data and tested on samples that follow a similar distribution,
neural networks have been shown to struggle with more advanced generalization
abilities, such as transferring knowledge across visually different domains, or
generalizing to new unseen combinations of known concepts. In this thesis we argue
that, in contrast to the usual black-box behavior of neural networks, leveraging
more structured internal representations is a promising direction\r\nfor tackling
such problems. In particular, we focus on two forms of structure. First, we tackle
modularity: We show that (i) compositional architectures are a natural tool for
modeling reasoning tasks, in that they efficiently capture their combinatorial
nature, which is key for generalizing beyond the compositions seen during training.
We investigate how to to learn such models, both formally and experimentally,
for the task of abstract visual reasoning. Then, we show that (ii) in some settings,
modularity allows us to efficiently break down complex tasks into smaller, easier,
modules, thereby improving computational efficiency; We study this behavior in
the context of generative models for colorization, as well as for small objects
detection. Secondly, we investigate the inherently layered structure of representations
learned by neural networks, and analyze its role in the context of transfer learning
and domain adaptation across visually\r\ndissimilar domains. "
acknowledged_ssus:
- _id: CampIT
- _id: ScienComp
acknowledgement: Last but not least, I would like to acknowledge the support of the
IST IT and scientific computing team for helping provide a great work environment.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
citation:
ama: Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning
models. 2020. doi:10.15479/AT:ISTA:8390
apa: Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible
Deep Learning models. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8390
chicago: Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible
Deep Learning Models.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8390.
ieee: A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep
Learning models,” Institute of Science and Technology Austria, 2020.
ista: Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible
Deep Learning models. Institute of Science and Technology Austria.
mla: Royer, Amélie. Leveraging Structure in Computer Vision Tasks for Flexible
Deep Learning Models. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8390.
short: A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep
Learning Models, Institute of Science and Technology Austria, 2020.
date_created: 2020-09-14T13:42:09Z
date_published: 2020-09-14T00:00:00Z
date_updated: 2023-10-16T10:04:02Z
day: '14'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:8390
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creator: dernst
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file_name: 2020_Thesis_Royer.pdf
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date_created: 2020-09-14T13:39:17Z
date_updated: 2020-09-14T13:39:17Z
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file_date_updated: 2020-09-14T13:39:17Z
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language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
month: '09'
oa: 1
oa_version: Published Version
page: '197'
publication_identifier:
isbn:
- 978-3-99078-007-7
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7936'
relation: part_of_dissertation
status: public
- id: '7937'
relation: part_of_dissertation
status: public
- id: '8193'
relation: part_of_dissertation
status: public
- id: '8092'
relation: part_of_dissertation
status: public
- id: '911'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Leveraging structure in Computer Vision tasks for flexible Deep Learning models
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...
---
_id: '8186'
abstract:
- lang: eng
text: "Numerous methods have been proposed for probabilistic generative modelling
of\r\n3D objects. However, none of these is able to produce textured objects,
which\r\nrenders them of limited use for practical tasks. In this work, we present
the\r\nfirst generative model of textured 3D meshes. Training such a model would\r\ntraditionally
require a large dataset of textured meshes, but unfortunately,\r\nexisting datasets
of meshes lack detailed textures. We instead propose a new\r\ntraining methodology
that allows learning from collections of 2D images without\r\nany 3D information.
To do so, we train our model to explain a distribution of\r\nimages by modelling
each image as a 3D foreground object placed in front of a\r\n2D background. Thus,
it learns to generate meshes that when rendered, produce\r\nimages similar to
those in its training set.\r\n A well-known problem when generating meshes with
deep networks is the\r\nemergence of self-intersections, which are problematic
for many use-cases. As a\r\nsecond contribution we therefore introduce a new generation
process for 3D\r\nmeshes that guarantees no self-intersections arise, based on
the physical\r\nintuition that faces should push one another out of the way as
they move.\r\n We conduct extensive experiments on our approach, reporting quantitative
and\r\nqualitative results on both synthetic data and natural images. These show
our\r\nmethod successfully learns to generate plausible and diverse textured 3D\r\nsamples
for five challenging object classes."
article_processing_charge: No
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Vagia
full_name: Tsiminaki, Vagia
last_name: Tsiminaki
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Henderson PM, Tsiminaki V, Lampert C. Leveraging 2D data to learn textured
3D mesh generation. In: Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition. IEEE; 2020:7498-7507. doi:10.1109/CVPR42600.2020.00752'
apa: 'Henderson, P. M., Tsiminaki, V., & Lampert, C. (2020). Leveraging 2D data
to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: IEEE.
https://doi.org/10.1109/CVPR42600.2020.00752'
chicago: Henderson, Paul M, Vagia Tsiminaki, and Christoph Lampert. “Leveraging
2D Data to Learn Textured 3D Mesh Generation.” In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 7498–7507. IEEE, 2020.
https://doi.org/10.1109/CVPR42600.2020.00752.
ieee: P. M. Henderson, V. Tsiminaki, and C. Lampert, “Leveraging 2D data to learn
textured 3D mesh generation,” in Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition, Virtual, 2020, pp. 7498–7507.
ista: 'Henderson PM, Tsiminaki V, Lampert C. 2020. Leveraging 2D data to learn textured
3D mesh generation. Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition,
7498–7507.'
mla: Henderson, Paul M., et al. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.”
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
IEEE, 2020, pp. 7498–507, doi:10.1109/CVPR42600.2020.00752.
short: P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.
conference:
end_date: 2020-06-19
location: Virtual
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2020-06-14
date_created: 2020-07-31T16:53:49Z
date_published: 2020-07-01T00:00:00Z
date_updated: 2023-10-17T07:37:11Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1109/CVPR42600.2020.00752
external_id:
arxiv:
- '2004.04180'
file:
- access_level: open_access
content_type: application/pdf
creator: phenders
date_created: 2020-07-31T16:57:12Z
date_updated: 2020-07-31T16:57:12Z
file_id: '8187'
file_name: paper.pdf
file_size: 10262773
relation: main_file
success: 1
file_date_updated: 2020-07-31T16:57:12Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openaccess.thecvf.com/content_CVPR_2020/papers/Henderson_Leveraging_2D_Data_to_Learn_Textured_3D_Mesh_Generation_CVPR_2020_paper.pdf
month: '07'
oa: 1
oa_version: Submitted Version
page: 7498-7507
publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition
publication_identifier:
eisbn:
- '9781728171685'
eissn:
- 2575-7075
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Leveraging 2D data to learn textured 3D mesh generation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '6944'
abstract:
- lang: eng
text: 'We study the problem of automatically detecting if a given multi-class classifier
operates outside of its specifications (out-of-specs), i.e. on input data from
a different distribution than what it was trained for. This is an important problem
to solve on the road towards creating reliable computer vision systems for real-world
applications, because the quality of a classifier’s predictions cannot be guaranteed
if it operates out-of-specs. Previously proposed methods for out-of-specs detection
make decisions on the level of single inputs. This, however, is insufficient to
achieve low false positive rate and high false negative rates at the same time.
In this work, we describe a new procedure named KS(conf), based on statistical
reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied
to the set of predicted confidence values for batches of samples. Working with
batches instead of single samples allows increasing the true positive rate without
negatively affecting the false positive rate, thereby overcoming a crucial limitation
of single sample tests. We show by extensive experiments using a variety of convolutional
network architectures and datasets that KS(conf) reliably detects out-of-specs
situations even under conditions where other tests fail. It furthermore has a
number of properties that make it an excellent candidate for practical deployment:
it is easy to implement, adds almost no overhead to the system, works with any
classifier that outputs confidence scores, and requires no a priori knowledge
about how the data distribution could change.'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Rémy
full_name: Sun, Rémy
last_name: Sun
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications. International Journal of Computer Vision.
2020;128(4):970-995. doi:10.1007/s11263-019-01232-x'
apa: 'Sun, R., & Lampert, C. (2020). KS(conf): A light-weight test if a multiclass
classifier operates outside of its specifications. International Journal of
Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01232-x'
chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a
Multiclass Classifier Operates Outside of Its Specifications.” International
Journal of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01232-x.'
ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications,” International Journal of Computer
Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.'
ista: 'Sun R, Lampert C. 2020. KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications. International Journal of Computer Vision.
128(4), 970–995.'
mla: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass
Classifier Operates Outside of Its Specifications.” International Journal of
Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.'
short: R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995.
date_created: 2019-10-14T09:14:28Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2024-02-22T14:57:30Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s11263-019-01232-x
ec_funded: 1
external_id:
isi:
- '000494406800001'
file:
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checksum: 155e63edf664dcacb3bdc1c2223e606f
content_type: application/pdf
creator: dernst
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date_updated: 2020-07-14T12:47:45Z
file_id: '7110'
file_name: 2019_IJCV_Sun.pdf
file_size: 1715072
relation: main_file
file_date_updated: 2020-07-14T12:47:45Z
has_accepted_license: '1'
intvolume: ' 128'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 970-995
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1007/s11263-019-01262-5
record:
- id: '6482'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: 'KS(conf): A light-weight test if a multiclass classifier operates outside
of its specifications'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 128
year: '2020'
...
---
_id: '7171'
abstract:
- lang: ger
text: "Wissen Sie, was sich hinter künstlicher Intelligenz und maschinellem Lernen
verbirgt? \r\nDieses Sachbuch erklärt Ihnen leicht verständlich und ohne komplizierte
Formeln die grundlegenden Methoden und Vorgehensweisen des maschinellen Lernens.
Mathematisches Vorwissen ist dafür nicht nötig. Kurzweilig und informativ illustriert
Lisa, die Protagonistin des Buches, diese anhand von Alltagssituationen. \r\nEin
Buch für alle, die in Diskussionen über Chancen und Risiken der aktuellen Entwicklung
der künstlichen Intelligenz und des maschinellen Lernens mit Faktenwissen punkten
möchten. Auch für Schülerinnen und Schüler geeignet!"
article_processing_charge: No
citation:
ama: 'Kersting K, Lampert C, Rothkopf C, eds. Wie Maschinen Lernen: Künstliche
Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature; 2019.
doi:10.1007/978-3-658-26763-6'
apa: 'Kersting, K., Lampert, C., & Rothkopf, C. (Eds.). (2019). Wie Maschinen
Lernen: Künstliche Intelligenz Verständlich Erklärt (1st ed.). Wiesbaden:
Springer Nature. https://doi.org/10.1007/978-3-658-26763-6'
chicago: 'Kersting, Kristian, Christoph Lampert, and Constantin Rothkopf, eds. Wie
Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden:
Springer Nature, 2019. https://doi.org/10.1007/978-3-658-26763-6.'
ieee: 'K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen:
Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature,
2019.'
ista: 'Kersting K, Lampert C, Rothkopf C eds. 2019. Wie Maschinen Lernen: Künstliche
Intelligenz Verständlich Erklärt 1st ed., Wiesbaden: Springer Nature, XIV, 245p.'
mla: 'Kersting, Kristian, et al., editors. Wie Maschinen Lernen: Künstliche Intelligenz
Verständlich Erklärt. 1st ed., Springer Nature, 2019, doi:10.1007/978-3-658-26763-6.'
short: 'K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche
Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.'
date_created: 2019-12-11T14:15:56Z
date_published: 2019-10-30T00:00:00Z
date_updated: 2021-12-22T14:40:58Z
day: '30'
department:
- _id: ChLa
doi: 10.1007/978-3-658-26763-6
edition: '1'
editor:
- first_name: Kristian
full_name: Kersting, Kristian
last_name: Kersting
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Constantin
full_name: Rothkopf, Constantin
last_name: Rothkopf
language:
- iso: ger
month: '10'
oa_version: None
page: XIV, 245
place: Wiesbaden
publication_identifier:
eisbn:
- 978-3-658-26763-6
isbn:
- 978-3-658-26762-9
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- description: News on IST Website
relation: press_release
url: https://ist.ac.at/en/news/book-release-how-machines-learn/
status: public
title: 'Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt'
type: book_editor
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2019'
...
---
_id: '6942'
abstract:
- lang: eng
text: "Graph games and Markov decision processes (MDPs) are standard models in reactive
synthesis and verification of probabilistic systems with nondeterminism. The class
of \U0001D714 -regular winning conditions; e.g., safety, reachability, liveness,
parity conditions; provides a robust and expressive specification formalism for
properties that arise in analysis of reactive systems. The resolutions of nondeterminism
in games and MDPs are represented as strategies, and we consider succinct representation
of such strategies. The decision-tree data structure from machine learning retains
the flavor of decisions of strategies and allows entropy-based minimization to
obtain succinct trees. However, in contrast to traditional machine-learning problems
where small errors are allowed, for winning strategies in graph games and MDPs
no error is allowed, and the decision tree must represent the entire strategy.
In this work we propose decision trees with linear classifiers for representation
of strategies in graph games and MDPs. We have implemented strategy representation
using this data structure and we present experimental results for problems on
graph games and MDPs, which show that this new data structure presents a much
more efficient strategy representation as compared to standard decision trees."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Pranav
full_name: Ashok, Pranav
last_name: Ashok
- first_name: Tomáš
full_name: Brázdil, Tomáš
last_name: Brázdil
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Jan
full_name: Křetínský, Jan
last_name: Křetínský
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Viktor
full_name: Toman, Viktor
id: 3AF3DA7C-F248-11E8-B48F-1D18A9856A87
last_name: Toman
orcid: 0000-0001-9036-063X
citation:
ama: 'Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. Strategy
representation by decision trees with linear classifiers. In: 16th International
Conference on Quantitative Evaluation of Systems. Vol 11785. Springer Nature;
2019:109-128. doi:10.1007/978-3-030-30281-8_7'
apa: 'Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., &
Toman, V. (2019). Strategy representation by decision trees with linear classifiers.
In 16th International Conference on Quantitative Evaluation of Systems
(Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7'
chicago: Ashok, Pranav, Tomáš Brázdil, Krishnendu Chatterjee, Jan Křetínský, Christoph
Lampert, and Viktor Toman. “Strategy Representation by Decision Trees with Linear
Classifiers.” In 16th International Conference on Quantitative Evaluation of
Systems, 11785:109–28. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-30281-8_7.
ieee: P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, and V. Toman,
“Strategy representation by decision trees with linear classifiers,” in 16th
International Conference on Quantitative Evaluation of Systems, Glasgow, United
Kingdom, 2019, vol. 11785, pp. 109–128.
ista: 'Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. 2019.
Strategy representation by decision trees with linear classifiers. 16th International
Conference on Quantitative Evaluation of Systems. QEST: Quantitative Evaluation
of Systems, LNCS, vol. 11785, 109–128.'
mla: Ashok, Pranav, et al. “Strategy Representation by Decision Trees with Linear
Classifiers.” 16th International Conference on Quantitative Evaluation of Systems,
vol. 11785, Springer Nature, 2019, pp. 109–28, doi:10.1007/978-3-030-30281-8_7.
short: P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, V. Toman,
in:, 16th International Conference on Quantitative Evaluation of Systems, Springer
Nature, 2019, pp. 109–128.
conference:
end_date: 2019-09-12
location: Glasgow, United Kingdom
name: 'QEST: Quantitative Evaluation of Systems'
start_date: 2019-09-10
date_created: 2019-10-14T06:57:49Z
date_published: 2019-09-04T00:00:00Z
date_updated: 2023-08-30T06:59:36Z
day: '04'
department:
- _id: KrCh
- _id: ChLa
doi: 10.1007/978-3-030-30281-8_7
external_id:
arxiv:
- '1906.08178'
isi:
- '000679281300007'
intvolume: ' 11785'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1906.08178
month: '09'
oa: 1
oa_version: Preprint
page: 109-128
project:
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25892FC0-B435-11E9-9278-68D0E5697425
grant_number: ICT15-003
name: Efficient Algorithms for Computer Aided Verification
publication: 16th International Conference on Quantitative Evaluation of Systems
publication_identifier:
eisbn:
- '9783030302818'
isbn:
- '9783030302801'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Strategy representation by decision trees with linear classifiers
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11785
year: '2019'
...
---
_id: '6554'
abstract:
- lang: eng
text: Due to the importance of zero-shot learning, i.e. classifying images where
there is a lack of labeled training data, the number of proposed approaches has
recently increased steadily. We argue that it is time to take a step back and
to analyze the status quo of the area. The purpose of this paper is three-fold.
First, given the fact that there is no agreed upon zero-shot learning benchmark,
we first define a new benchmark by unifying both the evaluation protocols and
data splits of publicly available datasets used for this task. This is an important
contribution as published results are often not comparable and sometimes even
flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose
a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset
which we make publicly available both in terms of image features and the images
themselves. Second, we compare and analyze a significant number of the state-of-the-art
methods in depth, both in the classic zero-shot setting but also in the more realistic
generalized zero-shot setting. Finally, we discuss in detail the limitations of
the current status of the area which can be taken as a basis for advancing it.
article_processing_charge: No
article_type: original
author:
- first_name: Yongqin
full_name: Xian, Yongqin
last_name: Xian
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0002-4561-241X
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Zeynep
full_name: Akata, Zeynep
last_name: Akata
citation:
ama: Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive
evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern
Analysis and Machine Intelligence. 2019;41(9):2251-2265. doi:10.1109/tpami.2018.2857768
apa: Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2019). Zero-shot learning
- A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions
on Pattern Analysis and Machine Intelligence. Institute of Electrical and
Electronics Engineers (IEEE). https://doi.org/10.1109/tpami.2018.2857768
chicago: Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot
Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” IEEE
Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical
and Electronics Engineers (IEEE), 2019. https://doi.org/10.1109/tpami.2018.2857768.
ieee: Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive
evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 41, no. 9. Institute of Electrical
and Electronics Engineers (IEEE), pp. 2251–2265, 2019.
ista: Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive
evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 41(9), 2251–2265.
mla: Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the
Good, the Bad and the Ugly.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 41, no. 9, Institute of Electrical and Electronics Engineers
(IEEE), 2019, pp. 2251–65, doi:10.1109/tpami.2018.2857768.
short: Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis
and Machine Intelligence 41 (2019) 2251–2265.
date_created: 2019-06-11T14:05:59Z
date_published: 2019-09-01T00:00:00Z
date_updated: 2023-09-05T13:18:09Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/tpami.2018.2857768
external_id:
arxiv:
- '1707.00600'
isi:
- '000480343900015'
intvolume: ' 41'
isi: 1
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1707.00600
month: '09'
oa: 1
oa_version: Preprint
page: 2251 - 2265
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
eissn:
- 1939-3539
issn:
- 0162-8828
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
quality_controlled: '1'
scopus_import: '1'
status: public
title: Zero-shot learning - A comprehensive evaluation of the good, the bad and the
ugly
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 41
year: '2019'
...
---
_id: '7479'
abstract:
- lang: eng
text: "Multi-exit architectures, in which a stack of processing layers is interleaved
with early output layers, allow the processing of a test example to stop early
and thus save computation time and/or energy. In this work, we propose a new
training procedure for multi-exit architectures based on the principle of knowledge
distillation. The method encourage searly exits to mimic later, more accurate
exits, by matching their output probabilities.\r\nExperiments on CIFAR100 and
\ ImageNet show that distillation-based training significantly improves the
accuracy of early exits while maintaining state-of-the-art accuracy for late
\ ones. The method is particularly beneficial when training data is limited
\ and it allows a straightforward extension to semi-supervised learning,i.e.
making use of unlabeled data at training time. Moreover, it takes only afew lines
to implement and incurs almost no computational overhead at training time, and
none at all at test time."
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. Distillation-based training for multi-exit architectures.
In: IEEE International Conference on Computer Vision. Vol 2019-October.
IEEE; 2019:1355-1364. doi:10.1109/ICCV.2019.00144'
apa: 'Phuong, M., & Lampert, C. (2019). Distillation-based training for multi-exit
architectures. In IEEE International Conference on Computer Vision (Vol.
2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144'
chicago: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit
Architectures.” In IEEE International Conference on Computer Vision, 2019–October:1355–64.
IEEE, 2019. https://doi.org/10.1109/ICCV.2019.00144.
ieee: M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,”
in IEEE International Conference on Computer Vision, Seoul, Korea, 2019,
vol. 2019–October, pp. 1355–1364.
ista: 'Phuong M, Lampert C. 2019. Distillation-based training for multi-exit architectures.
IEEE International Conference on Computer Vision. ICCV: International Conference
on Computer Vision vol. 2019–October, 1355–1364.'
mla: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit
Architectures.” IEEE International Conference on Computer Vision, vol.
2019–October, IEEE, 2019, pp. 1355–64, doi:10.1109/ICCV.2019.00144.
short: M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision,
IEEE, 2019, pp. 1355–1364.
conference:
end_date: 2019-11-02
location: Seoul, Korea
name: 'ICCV: International Conference on Computer Vision'
start_date: 2019-10-27
date_created: 2020-02-11T09:06:57Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2023-09-08T11:11:12Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1109/ICCV.2019.00144
ec_funded: 1
external_id:
isi:
- '000531438101047'
file:
- access_level: open_access
checksum: 7b77fb5c2d27c4c37a7612ba46a66117
content_type: application/pdf
creator: bphuong
date_created: 2020-02-11T09:06:39Z
date_updated: 2020-07-14T12:47:59Z
file_id: '7480'
file_name: main.pdf
file_size: 735768
relation: main_file
file_date_updated: 2020-07-14T12:47:59Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 1355-1364
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: IEEE International Conference on Computer Vision
publication_identifier:
isbn:
- '9781728148038'
issn:
- '15505499'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '9418'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Distillation-based training for multi-exit architectures
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2019-October
year: '2019'
...
---
_id: '7640'
abstract:
- lang: eng
text: We propose a new model for detecting visual relationships, such as "person
riding motorcycle" or "bottle on table". This task is an important step towards
comprehensive structured mage understanding, going beyond detecting individual
objects. Our main novelty is a Box Attention mechanism that allows to model pairwise
interactions between objects using standard object detection pipelines. The resulting
model is conceptually clean, expressive and relies on well-justified training
and prediction procedures. Moreover, unlike previously proposed approaches, our
model does not introduce any additional complex components or hyperparameters
on top of those already required by the underlying detection model. We conduct
an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating
strong quantitative and qualitative results.
article_number: 1749-1753
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Alina
full_name: Kuznetsova, Alina
last_name: Kuznetsova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships
using box attention. In: Proceedings of the 2019 International Conference on
Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00217'
apa: 'Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting
visual relationships using box attention. In Proceedings of the 2019 International
Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217'
chicago: Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio
Ferrari. “Detecting Visual Relationships Using Box Attention.” In Proceedings
of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019.
https://doi.org/10.1109/ICCVW.2019.00217.
ieee: A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual
relationships using box attention,” in Proceedings of the 2019 International
Conference on Computer Vision Workshop, Seoul, South Korea, 2019.
ista: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual
relationships using box attention. Proceedings of the 2019 International Conference
on Computer Vision Workshop. ICCVW: International Conference on Computer Vision
Workshop, 1749–1753.'
mla: Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.”
Proceedings of the 2019 International Conference on Computer Vision Workshop,
1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.
short: A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of
the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
conference:
end_date: 2019-10-28
location: Seoul, South Korea
name: 'ICCVW: International Conference on Computer Vision Workshop'
start_date: 2019-10-27
date_created: 2020-04-05T22:00:51Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2023-09-08T11:18:37Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCVW.2019.00217
ec_funded: 1
external_id:
arxiv:
- '1807.02136'
isi:
- '000554591601098'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1807.02136
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 2019 International Conference on Computer Vision Workshop
publication_identifier:
isbn:
- '9781728150239'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Detecting visual relationships using box attention
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
_id: '6569'
abstract:
- lang: eng
text: 'Knowledge distillation, i.e. one classifier being trained on the outputs
of another classifier, is an empirically very successful technique for knowledge
transfer between classifiers. It has even been observed that classifiers learn
much faster and more reliably if trained with the outputs of another classifier
as soft labels, instead of from ground truth data. So far, however, there is no
satisfactory theoretical explanation of this phenomenon. In this work, we provide
the first insights into the working mechanisms of distillation by studying the
special case of linear and deep linear classifiers. Specifically, we prove a
generalization bound that establishes fast convergence of the expected risk of
a distillation-trained linear classifier. From the bound and its proof we extract
three keyfactors that determine the success of distillation: data geometry – geometric
properties of the datadistribution, in particular class separation, has an immediate
influence on the convergence speed of the risk; optimization bias– gradient descentoptimization
finds a very favorable minimum of the distillation objective; and strong monotonicity–
the expected risk of the student classifier always decreases when the size of
the training set grows.'
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. Towards understanding knowledge distillation. In: Proceedings
of the 36th International Conference on Machine Learning. Vol 97. ML Research
Press; 2019:5142-5151.'
apa: 'Phuong, M., & Lampert, C. (2019). Towards understanding knowledge distillation.
In Proceedings of the 36th International Conference on Machine Learning
(Vol. 97, pp. 5142–5151). Long Beach, CA, United States: ML Research Press.'
chicago: Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.”
In Proceedings of the 36th International Conference on Machine Learning,
97:5142–51. ML Research Press, 2019.
ieee: M. Phuong and C. Lampert, “Towards understanding knowledge distillation,”
in Proceedings of the 36th International Conference on Machine Learning,
Long Beach, CA, United States, 2019, vol. 97, pp. 5142–5151.
ista: 'Phuong M, Lampert C. 2019. Towards understanding knowledge distillation.
Proceedings of the 36th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 97, 5142–5151.'
mla: Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.”
Proceedings of the 36th International Conference on Machine Learning, vol.
97, ML Research Press, 2019, pp. 5142–51.
short: M. Phuong, C. Lampert, in:, Proceedings of the 36th International Conference
on Machine Learning, ML Research Press, 2019, pp. 5142–5151.
conference:
end_date: 2019-06-15
location: Long Beach, CA, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2019-06-10
date_created: 2019-06-20T18:23:03Z
date_published: 2019-06-13T00:00:00Z
date_updated: 2023-10-17T12:31:38Z
day: '13'
ddc:
- '000'
department:
- _id: ChLa
file:
- access_level: open_access
checksum: a66d00e2694d749250f8507f301320ca
content_type: application/pdf
creator: bphuong
date_created: 2019-06-20T18:22:56Z
date_updated: 2020-07-14T12:47:33Z
file_id: '6570'
file_name: paper.pdf
file_size: 686432
relation: main_file
file_date_updated: 2020-07-14T12:47:33Z
has_accepted_license: '1'
intvolume: ' 97'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 5142-5151
publication: Proceedings of the 36th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards understanding knowledge distillation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2019'
...
---
_id: '6590'
abstract:
- lang: eng
text: 'Modern machine learning methods often require more data for training than
a single expert can provide. Therefore, it has become a standard procedure to
collect data from external sources, e.g. via crowdsourcing. Unfortunately, the
quality of these sources is not always guaranteed. As additional complications,
the data might be stored in a distributed way, or might even have to remain private.
In this work, we address the question of how to learn robustly in such scenarios.
Studying the problem through the lens of statistical learning theory, we derive
a procedure that allows for learning from all available sources, yet automatically
suppresses irrelevant or corrupted data. We show by extensive experiments that
our method provides significant improvements over alternative approaches from
robust statistics and distributed optimization. '
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: Proceedings
of the 36th International Conference on Machine Learning. Vol 97. ML Research
Press; 2019:3488-3498.'
apa: 'Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted
sources. In Proceedings of the 36th International Conference on Machine Learning
(Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.'
chicago: Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted
Sources.” In Proceedings of the 36th International Conference on Machine Learning,
97:3488–98. ML Research Press, 2019.
ieee: N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,”
in Proceedings of the 36th International Conference on Machine Learning,
Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
ista: 'Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources.
Proceedings of the 36th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 97, 3488–3498.'
mla: Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted
Sources.” Proceedings of the 36th International Conference on Machine Learning,
vol. 97, ML Research Press, 2019, pp. 3488–98.
short: N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International
Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.
conference:
end_date: 2919-06-15
location: Long Beach, CA, USA
name: 'ICML: International Conference on Machine Learning'
start_date: 2019-06-10
date_created: 2019-06-27T14:18:23Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2023-10-17T12:31:55Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1901.10310'
intvolume: ' 97'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1901.10310
month: '06'
oa: 1
oa_version: Preprint
page: 3488-3498
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 36th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '10799'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Robust learning from untrusted sources
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2019'
...
---
_id: '6482'
abstract:
- lang: eng
text: 'Computer vision systems for automatic image categorization have become accurate
and reliable enough that they can run continuously for days or even years as components
of real-world commercial applications. A major open problem in this context, however,
is quality control. Good classification performance can only be expected if systems
run under the specific conditions, in particular data distributions, that they
were trained for. Surprisingly, none of the currently used deep network architectures
have a built-in functionality that could detect if a network operates on data
from a distribution it was not trained for, such that potentially a warning to
the human users could be triggered. In this work, we describe KS(conf), a procedure
for detecting such outside of specifications (out-of-specs) operation, based on
statistical testing of the network outputs. We show by extensive experiments using
the ImageNet, AwA2 and DAVIS datasets on a variety of ConvNets architectures that
KS(conf) reliably detects out-of-specs situations. It furthermore has a number
of properties that make it a promising candidate for practical deployment: it
is easy to implement, adds almost no overhead to the system, works with all networks,
including pretrained ones, and requires no a priori knowledge of how the data
distribution could change. '
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Rémy
full_name: Sun, Rémy
last_name: Sun
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a ConvNet operates outside
of Its specifications. In: Vol 11269. Springer Nature; 2019:244-259. doi:10.1007/978-3-030-12939-2_18'
apa: 'Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet
operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at
the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature.
https://doi.org/10.1007/978-3-030-12939-2_18'
chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a
ConvNet Operates Outside of Its Specifications,” 11269:244–59. Springer Nature,
2019. https://doi.org/10.1007/978-3-030-12939-2_18.'
ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates
outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition,
Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.'
ista: 'Sun R, Lampert C. 2019. KS(conf): A light-weight test if a ConvNet operates
outside of Its specifications. GCPR: Conference on Pattern Recognition, LNCS,
vol. 11269, 244–259.'
mla: 'Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet
Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019,
pp. 244–59, doi:10.1007/978-3-030-12939-2_18.'
short: R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.
conference:
end_date: 2018-10-12
location: Stuttgart, Germany
name: 'GCPR: Conference on Pattern Recognition'
start_date: 2018-10-09
date_created: 2019-05-24T09:48:36Z
date_published: 2019-02-14T00:00:00Z
date_updated: 2024-02-22T14:57:29Z
day: '14'
department:
- _id: ChLa
doi: 10.1007/978-3-030-12939-2_18
ec_funded: 1
external_id:
arxiv:
- '1804.04171'
intvolume: ' 11269'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1804.04171
month: '02'
oa: 1
oa_version: Preprint
page: 244-259
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030129385'
- '9783030129392'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '6944'
relation: later_version
status: public
scopus_import: '1'
status: public
title: 'KS(conf): A light-weight test if a ConvNet operates outside of Its specifications'
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 11269
year: '2019'
...
---
_id: '68'
abstract:
- lang: eng
text: The most common assumption made in statistical learning theory is the assumption
of the independent and identically distributed (i.i.d.) data. While being very
convenient mathematically, it is often very clearly violated in practice. This
disparity between the machine learning theory and applications underlies a growing
demand in the development of algorithms that learn from dependent data and theory
that can provide generalization guarantees similar to the independent situations.
This thesis is dedicated to two variants of dependencies that can arise in practice.
One is a dependence on the level of samples in a single learning task. Another
dependency type arises in the multi-task setting when the tasks are dependent
on each other even though the data for them can be i.i.d. In both cases we model
the data (samples or tasks) as stochastic processes and introduce new algorithms
for both settings that take into account and exploit the resulting dependencies.
We prove the theoretical guarantees on the performance of the introduced algorithms
under different evaluation criteria and, in addition, we compliment the theoretical
study by the empirical one, where we evaluate some of the algorithms on two real
world datasets to highlight their practical applicability.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Zimin, Alexander
id: 37099E9C-F248-11E8-B48F-1D18A9856A87
last_name: Zimin
citation:
ama: Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048
apa: Zimin, A. (2018). Learning from dependent data. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048
chicago: Zimin, Alexander. “Learning from Dependent Data.” Institute of Science
and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:TH1048.
ieee: A. Zimin, “Learning from dependent data,” Institute of Science and Technology
Austria, 2018.
ista: Zimin A. 2018. Learning from dependent data. Institute of Science and Technology
Austria.
mla: Zimin, Alexander. Learning from Dependent Data. Institute of Science
and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.
short: A. Zimin, Learning from Dependent Data, Institute of Science and Technology
Austria, 2018.
date_created: 2018-12-11T11:44:27Z
date_published: 2018-09-01T00:00:00Z
date_updated: 2023-09-07T12:29:07Z
day: '01'
ddc:
- '004'
- '519'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH1048
ec_funded: 1
file:
- access_level: open_access
checksum: e849dd40a915e4d6c5572b51b517f098
content_type: application/pdf
creator: dernst
date_created: 2019-04-09T07:32:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6253'
file_name: 2018_Thesis_Zimin.pdf
file_size: 1036137
relation: main_file
- access_level: closed
checksum: da092153cec55c97461bd53c45c5d139
content_type: application/zip
creator: dernst
date_created: 2019-04-09T07:32:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6254'
file_name: 2018_Thesis_Zimin_Source.zip
file_size: 637490
relation: source_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '92'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7986'
pubrep_id: '1048'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Learning from dependent data
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '197'
abstract:
- lang: eng
text: Modern computer vision systems heavily rely on statistical machine learning
models, which typically require large amounts of labeled data to be learned reliably.
Moreover, very recently computer vision research widely adopted techniques for
representation learning, which further increase the demand for labeled data. However,
for many important practical problems there is relatively small amount of labeled
data available, so it is problematic to leverage full potential of the representation
learning methods. One way to overcome this obstacle is to invest substantial resources
into producing large labelled datasets. Unfortunately, this can be prohibitively
expensive in practice. In this thesis we focus on the alternative way of tackling
the aforementioned issue. We concentrate on methods, which make use of weakly-labeled
or even unlabeled data. Specifically, the first half of the thesis is dedicated
to the semantic image segmentation task. We develop a technique, which achieves
competitive segmentation performance and only requires annotations in a form of
global image-level labels instead of dense segmentation masks. Subsequently, we
present a new methodology, which further improves segmentation performance by
leveraging tiny additional feedback from a human annotator. By using our methods
practitioners can greatly reduce the amount of data annotation effort, which is
required to learn modern image segmentation models. In the second half of the
thesis we focus on methods for learning from unlabeled visual data. We study a
family of autoregressive models for modeling structure of natural images and discuss
potential applications of these models. Moreover, we conduct in-depth study of
one of these applications, where we develop the state-of-the-art model for the
probabilistic image colorization task.
acknowledgement: I also gratefully acknowledge the support of NVIDIA Corporation with
the donation of the GPUs used for this research.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
citation:
ama: Kolesnikov A. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural
Images. 2018. doi:10.15479/AT:ISTA:th_1021
apa: Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
chicago: Kolesnikov, Alexander. “Weakly-Supervised Segmentation and Unsupervised
Modeling of Natural Images.” Institute of Science and Technology Austria, 2018.
https://doi.org/10.15479/AT:ISTA:th_1021.
ieee: A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images,” Institute of Science and Technology Austria, 2018.
ista: Kolesnikov A. 2018. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria.
mla: Kolesnikov, Alexander. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_1021.
short: A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images, Institute of Science and Technology Austria, 2018.
date_created: 2018-12-11T11:45:09Z
date_published: 2018-05-25T00:00:00Z
date_updated: 2023-09-07T12:51:46Z
day: '25'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:th_1021
ec_funded: 1
file:
- access_level: open_access
checksum: bc678e02468d8ebc39dc7267dfb0a1c4
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:57Z
date_updated: 2020-07-14T12:45:22Z
file_id: '5113'
file_name: IST-2018-1021-v1+1_thesis-unsigned-pdfa.pdf
file_size: 12918758
relation: main_file
- access_level: closed
checksum: bc66973b086da5a043f1162dcfb1fde4
content_type: application/zip
creator: dernst
date_created: 2019-04-05T09:34:49Z
date_updated: 2020-07-14T12:45:22Z
file_id: '6225'
file_name: 2018_Thesis_Kolesnikov_source.zip
file_size: 55973760
relation: source_file
file_date_updated: 2020-07-14T12:45:22Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '113'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7718'
pubrep_id: '1021'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '563'
abstract:
- lang: eng
text: "In continuous populations with local migration, nearby pairs of individuals
have on average more similar genotypes\r\nthan geographically well separated pairs.
A barrier to gene flow distorts this classical pattern of isolation by distance.
Genetic similarity is decreased for sample pairs on different sides of the barrier
and increased for pairs on the same side near the barrier. Here, we introduce
an inference scheme that utilizes this signal to detect and estimate the strength
of a linear barrier to gene flow in two-dimensions. We use a diffusion approximation
to model the effects of a barrier on the geographical spread of ancestry backwards
in time. This approach allows us to calculate the chance of recent coalescence
and probability of identity by descent. We introduce an inference scheme that
fits these theoretical results to the geographical covariance structure of bialleleic
genetic markers. It can estimate the strength of the barrier as well as several
demographic parameters. We investigate the power of our inference scheme to detect
barriers by applying it to a wide range of simulated data. We also showcase an
example application to a Antirrhinum majus (snapdragon) flower color hybrid zone,
where we do not detect any signal of a strong genome wide barrier to gene flow."
article_processing_charge: No
author:
- first_name: Harald
full_name: Ringbauer, Harald
id: 417FCFF4-F248-11E8-B48F-1D18A9856A87
last_name: Ringbauer
orcid: 0000-0002-4884-9682
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: David
full_name: Field, David
last_name: Field
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Ringbauer H, Kolesnikov A, Field D, Barton NH. Estimating barriers to gene
flow from distorted isolation-by-distance patterns. Genetics. 2018;208(3):1231-1245.
doi:10.1534/genetics.117.300638
apa: Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating
barriers to gene flow from distorted isolation-by-distance patterns. Genetics.
Genetics Society of America. https://doi.org/10.1534/genetics.117.300638
chicago: Ringbauer, Harald, Alexander Kolesnikov, David Field, and Nicholas H Barton.
“Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.”
Genetics. Genetics Society of America, 2018. https://doi.org/10.1534/genetics.117.300638.
ieee: H. Ringbauer, A. Kolesnikov, D. Field, and N. H. Barton, “Estimating barriers
to gene flow from distorted isolation-by-distance patterns,” Genetics,
vol. 208, no. 3. Genetics Society of America, pp. 1231–1245, 2018.
ista: Ringbauer H, Kolesnikov A, Field D, Barton NH. 2018. Estimating barriers to
gene flow from distorted isolation-by-distance patterns. Genetics. 208(3), 1231–1245.
mla: Ringbauer, Harald, et al. “Estimating Barriers to Gene Flow from Distorted
Isolation-by-Distance Patterns.” Genetics, vol. 208, no. 3, Genetics Society
of America, 2018, pp. 1231–45, doi:10.1534/genetics.117.300638.
short: H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
date_created: 2018-12-11T11:47:12Z
date_published: 2018-03-01T00:00:00Z
date_updated: 2023-09-11T13:42:38Z
day: '01'
department:
- _id: NiBa
- _id: ChLa
doi: 10.1534/genetics.117.300638
external_id:
isi:
- '000426219600025'
intvolume: ' 208'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.biorxiv.org/content/10.1101/205484v1
month: '03'
oa: 1
oa_version: Preprint
page: 1231-1245
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '7251'
quality_controlled: '1'
related_material:
record:
- id: '200'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Estimating barriers to gene flow from distorted isolation-by-distance patterns
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 208
year: '2018'
...
---
_id: '321'
abstract:
- lang: eng
text: The twelve papers in this special section focus on learning systems with shared
information for computer vision and multimedia communication analysis. In the
real world, a realistic setting for computer vision or multimedia recognition
problems is that we have some classes containing lots of training data and many
classes containing a small amount of training data. Therefore, how to use frequent
classes to help learning rare classes for which it is harder to collect the training
data is an open question. Learning with shared information is an emerging topic
in machine learning, computer vision and multimedia analysis. There are different
levels of components that can be shared during concept modeling and machine learning
stages, such as sharing generic object parts, sharing attributes, sharing transformations,
sharing regularization parameters and sharing training examples, etc. Regarding
the specific methods, multi-task learning, transfer learning and deep learning
can be seen as using different strategies to share information. These learning
with shared information methods are very effective in solving real-world large-scale
problems.
article_processing_charge: No
article_type: original
author:
- first_name: Trevor
full_name: Darrell, Trevor
last_name: Darrell
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Nico
full_name: Sebe, Nico
last_name: Sebe
- first_name: Ying
full_name: Wu, Ying
last_name: Wu
- first_name: Yan
full_name: Yan, Yan
last_name: Yan
citation:
ama: Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. Guest editors’ introduction to the
special section on learning with Shared information for computer vision and multimedia
analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
2018;40(5):1029-1031. doi:10.1109/TPAMI.2018.2804998
apa: Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). Guest editors’
introduction to the special section on learning with Shared information for computer
vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine
Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2018.2804998
chicago: Darrell, Trevor, Christoph Lampert, Nico Sebe, Ying Wu, and Yan Yan. “Guest
Editors’ Introduction to the Special Section on Learning with Shared Information
for Computer Vision and Multimedia Analysis.” IEEE Transactions on Pattern
Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2018.2804998.
ieee: T. Darrell, C. Lampert, N. Sebe, Y. Wu, and Y. Yan, “Guest editors’ introduction
to the special section on learning with Shared information for computer vision
and multimedia analysis,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 40, no. 5. IEEE, pp. 1029–1031, 2018.
ista: Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. 2018. Guest editors’ introduction
to the special section on learning with Shared information for computer vision
and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
40(5), 1029–1031.
mla: Darrell, Trevor, et al. “Guest Editors’ Introduction to the Special Section
on Learning with Shared Information for Computer Vision and Multimedia Analysis.”
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40,
no. 5, IEEE, 2018, pp. 1029–31, doi:10.1109/TPAMI.2018.2804998.
short: T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern
Analysis and Machine Intelligence 40 (2018) 1029–1031.
date_created: 2018-12-11T11:45:48Z
date_published: 2018-05-01T00:00:00Z
date_updated: 2023-09-11T14:07:54Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1109/TPAMI.2018.2804998
external_id:
isi:
- '000428901200001'
file:
- access_level: open_access
checksum: b19c75da06faf3291a3ca47dfa50ef63
content_type: application/pdf
creator: dernst
date_created: 2020-05-14T12:50:48Z
date_updated: 2020-07-14T12:46:03Z
file_id: '7835'
file_name: 2018_IEEE_Darrell.pdf
file_size: 141724
relation: main_file
file_date_updated: 2020-07-14T12:46:03Z
has_accepted_license: '1'
intvolume: ' 40'
isi: 1
issue: '5'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 1029 - 1031
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '7544'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Guest editors' introduction to the special section on learning with Shared
information for computer vision and multimedia analysis
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 40
year: '2018'
...
---
_id: '10882'
abstract:
- lang: eng
text: 'We introduce Intelligent Annotation Dialogs for bounding box annotation.
We train an agent to automatically choose a sequence of actions for a human annotator
to produce a bounding box in a minimal amount of time. Specifically, we consider
two actions: box verification [34], where the annotator verifies a box generated
by an object detector, and manual box drawing. We explore two kinds of agents,
one based on predicting the probability that a box will be positively verified,
and the other based on reinforcement learning. We demonstrate that (1) our agents
are able to learn efficient annotation strategies in several scenarios, automatically
adapting to the image difficulty, the desired quality of the boxes, and the detector
strength; (2) in all scenarios the resulting annotation dialogs speed up annotation
compared to manual box drawing alone and box verification alone, while also outperforming
any fixed combination of verification and drawing in most scenarios; (3) in a
realistic scenario where the detector is iteratively re-trained, our agents evolve
a series of strategies that reflect the shifting trade-off between verification
and drawing as the detector grows stronger.'
article_processing_charge: No
author:
- first_name: Jasper
full_name: Uijlings, Jasper
last_name: Uijlings
- first_name: Ksenia
full_name: Konyushkova, Ksenia
last_name: Konyushkova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs
for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956'
apa: 'Uijlings, J., Konyushkova, K., Lampert, C., & Ferrari, V. (2018). Learning
intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City,
UT, United States: IEEE. https://doi.org/10.1109/cvpr.2018.00956'
chicago: Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari.
“Learning Intelligent Dialogs for Bounding Box Annotation.” In 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 9175–84. IEEE, 2018.
https://doi.org/10.1109/cvpr.2018.00956.
ieee: J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent
dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp.
9175–9184.
ista: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent
dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition,
9175–9184.'
mla: Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.”
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE,
2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956.
short: J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.
conference:
end_date: 2018-06-23
location: Salt Lake City, UT, United States
name: 'CVF: Conference on Computer Vision and Pattern Recognition'
start_date: 2018-06-18
date_created: 2022-03-18T12:45:09Z
date_published: 2018-12-17T00:00:00Z
date_updated: 2023-09-19T15:11:49Z
day: '17'
department:
- _id: ChLa
doi: 10.1109/cvpr.2018.00956
external_id:
arxiv:
- '1712.08087'
isi:
- '000457843609036'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.1712.08087'
month: '12'
oa: 1
oa_version: Preprint
page: 9175-9184
publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781538664209'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning intelligent dialogs for bounding box annotation
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '6012'
abstract:
- lang: eng
text: We present an approach to identify concise equations from data using a shallow
neural network approach. In contrast to ordinary black-box regression, this approach
allows understanding functional relations and generalizing them from observed
data to unseen parts of the parameter space. We show how to extend the class of
learnable equations for a recently proposed equation learning network to include
divisions, and we improve the learning and model selection strategy to be useful
for challenging real-world data. For systems governed by analytical expressions,
our method can in many cases identify the true underlying equation and extrapolate
to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum
system, where only 2 random rollouts are required to learn the forward dynamics
and successfully achieve the swing-up task.
article_processing_charge: No
author:
- first_name: Subham
full_name: Sahoo, Subham
last_name: Sahoo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: 'Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control.
In: Proceedings of the 35th International Conference on Machine Learning.
Vol 80. ML Research Press; 2018:4442-4450.'
apa: 'Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for
extrapolation and control. In Proceedings of the 35th International Conference
on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research
Press.'
chicago: Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations
for Extrapolation and Control.” In Proceedings of the 35th International Conference
on Machine Learning, 80:4442–50. ML Research Press, 2018.
ieee: S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation
and control,” in Proceedings of the 35th International Conference on Machine
Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
ista: 'Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation
and control. Proceedings of the 35th International Conference on Machine Learning.
ICML: International Conference on Machine Learning vol. 80, 4442–4450.'
mla: Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings
of the 35th International Conference on Machine Learning, vol. 80, ML Research
Press, 2018, pp. 4442–50.
short: S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International
Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: 'ICML: International Conference on Machine Learning'
start_date: 2018-07-10
date_created: 2019-02-14T15:21:07Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2023-10-17T09:50:53Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1806.07259'
isi:
- '000683379204058'
intvolume: ' 80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.07259
month: '02'
oa: 1
oa_version: Preprint
page: 4442-4450
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 35th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/first-machine-learning-method-capable-of-accurate-extrapolation/
scopus_import: '1'
status: public
title: Learning equations for extrapolation and control
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '6011'
abstract:
- lang: eng
text: 'We establish a data-dependent notion of algorithmic stability for Stochastic
Gradient Descent (SGD), and employ it to develop novel generalization bounds.
This is in contrast to previous distribution-free algorithmic stability results
for SGD which depend on the worst-case constants. By virtue of the data-dependent
argument, our bounds provide new insights into learning with SGD on convex and
non-convex problems. In the convex case, we show that the bound on the generalization
error depends on the risk at the initialization point. In the non-convex case,
we prove that the expected curvature of the objective function around the initialization
point has crucial influence on the generalization error. In both cases, our results
suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization.
As a corollary, our results allow us to show optimistic generalization bounds
that exhibit fast convergence rates for SGD subject to a vanishing empirical risk
and low noise of stochastic gradient. '
article_processing_charge: No
author:
- first_name: Ilja
full_name: Kuzborskij, Ilja
last_name: Kuzborskij
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent.
In: Proceedings of the 35 Th International Conference on Machine Learning.
Vol 80. ML Research Press; 2018:2815-2824.'
apa: 'Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic
gradient descent. In Proceedings of the 35 th International Conference on Machine
Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.'
chicago: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic
Gradient Descent.” In Proceedings of the 35 Th International Conference on
Machine Learning, 80:2815–24. ML Research Press, 2018.
ieee: I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient
descent,” in Proceedings of the 35 th International Conference on Machine Learning,
Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.
ista: 'Kuzborskij I, Lampert C. 2018. Data-dependent stability of stochastic gradient
descent. Proceedings of the 35 th International Conference on Machine Learning.
ICML: International Conference on Machine Learning vol. 80, 2815–2824.'
mla: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic
Gradient Descent.” Proceedings of the 35 Th International Conference on Machine
Learning, vol. 80, ML Research Press, 2018, pp. 2815–24.
short: I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference
on Machine Learning, ML Research Press, 2018, pp. 2815–2824.
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: 'ICML: International Conference on Machine Learning'
start_date: 2018-07-10
date_created: 2019-02-14T14:51:57Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2023-10-17T09:51:13Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1703.01678'
isi:
- '000683379202095'
intvolume: ' 80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1703.01678
month: '02'
oa: 1
oa_version: Preprint
page: 2815-2824
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 35 th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Data-dependent stability of stochastic gradient descent
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '6589'
abstract:
- lang: eng
text: Distributed training of massive machine learning models, in particular deep
neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace.
Several families of communication-reduction methods, such as quantization, large-batch
methods, and gradient sparsification, have been proposed. To date, gradient sparsification
methods--where each node sorts gradients by magnitude, and only communicates a
subset of the components, accumulating the rest locally--are known to yield some
of the largest practical gains. Such methods can reduce the amount of communication
per step by up to \emph{three orders of magnitude}, while preserving model accuracy.
Yet, this family of methods currently has no theoretical justification. This is
the question we address in this paper. We prove that, under analytic assumptions,
sparsifying gradients by magnitude with local error correction provides convergence
guarantees, for both convex and non-convex smooth objectives, for data-parallel
SGD. The main insight is that sparsification methods implicitly maintain bounds
on the maximum impact of stale updates, thanks to selection by magnitude. Our
analysis and empirical validation also reveal that these methods do require analytical
conditions to converge well, justifying existing heuristics.
article_processing_charge: No
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
- first_name: Mikael
full_name: Johansson, Mikael
last_name: Johansson
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Sarit
full_name: Khirirat, Sarit
last_name: Khirirat
- first_name: Cedric
full_name: Renggli, Cedric
last_name: Renggli
citation:
ama: 'Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli
C. The convergence of sparsified gradient methods. In: Advances in Neural Information
Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems
Foundation; 2018:5973-5983.'
apa: 'Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat,
S., & Renggli, C. (2018). The convergence of sparsified gradient methods.
In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018,
pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.'
chicago: Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov,
Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.”
In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83.
Neural Information Processing Systems Foundation, 2018.
ieee: D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat,
and C. Renggli, “The convergence of sparsified gradient methods,” in Advances
in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol.
Volume 2018, pp. 5973–5983.
ista: 'Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli
C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information
Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems
vol. Volume 2018, 5973–5983.'
mla: Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.”
Advances in Neural Information Processing Systems 31, vol. Volume 2018,
Neural Information Processing Systems Foundation, 2018, pp. 5973–83.
short: D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat,
C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural
Information Processing Systems Foundation, 2018, pp. 5973–5983.
conference:
end_date: 2018-12-08
location: Montreal, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2018-12-02
date_created: 2019-06-27T09:32:55Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2023-10-17T11:47:20Z
day: '01'
department:
- _id: DaAl
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1809.10505'
isi:
- '000461852000047'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1809.10505
month: '12'
oa: 1
oa_version: Preprint
page: 5973-5983
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Advances in Neural Information Processing Systems 31
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: The convergence of sparsified gradient methods
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: Volume 2018
year: '2018'
...
---
_id: '5584'
abstract:
- lang: eng
text: "This package contains data for the publication \"Nonlinear decoding of a
complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018).
\r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion
cells that pass stability and quality criteria, recorded on the multi-electrode
array, in response to the presentation of the complex movie with many randomly
moving dark discs. The responses are represented as 648000 x 91 binary matrix,
where the first index indicates the timebin of duration 12.5 ms, and the second
index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike
in the particular time bin.\r\n(ii) README file and a graphical illustration of
the structure of the experiment, specifying how the 648000 timebins are split
into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments
are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix
of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie
frame, with time that is locked to the recorded raster. The luminance traces are
produced as described in the manuscript by filtering the raw disc movie with a
small gaussian spatial kernel. "
article_processing_charge: No
author:
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella-Soler, Vicente
last_name: Botella-Soler
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding
of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98
apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018).
Nonlinear decoding of a complex movie from the mammalian retina. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:98
chicago: Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius,
and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:98.
ieee: S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear
decoding of a complex movie from the mammalian retina.” Institute of Science and
Technology Austria, 2018.
ista: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding
of a complex movie from the mammalian retina, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:98.
mla: Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian
Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98.
short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
datarep_id: '98'
date_created: 2018-12-12T12:31:39Z
date_published: 2018-03-29T00:00:00Z
date_updated: 2024-02-21T13:45:26Z
day: '29'
ddc:
- '570'
department:
- _id: ChLa
- _id: GaTk
doi: 10.15479/AT:ISTA:98
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file_size: 986
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has_accepted_license: '1'
keyword:
- retina
- decoding
- regression
- neural networks
- complex stimulus
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '292'
relation: used_in_publication
status: public
status: public
title: Nonlinear decoding of a complex movie from the mammalian retina
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '652'
abstract:
- lang: eng
text: 'We present an approach that enables robots to self-organize their sensorimotor
behavior from scratch without providing specific information about neither the
robot nor its environment. This is achieved by a simple neural control law that
increases the consistency between external sensor dynamics and internal neural
dynamics of the utterly simple controller. In this way, the embodiment and the
agent-environment coupling are the only source of individual development. We show
how an anthropomorphic tendon driven arm-shoulder system develops different behaviors
depending on that coupling. For instance: Given a bottle half-filled with water,
the arm starts to shake it, driven by the physical response of the water. When
attaching a brush, the arm can be manipulated into wiping a table, and when connected
to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said
to discover the affordances of the world. When allowing two (simulated) humanoid
robots to interact physically, they engage into a joint behavior development leading
to, for instance, spontaneous cooperation. More social effects are observed if
the robots can visually perceive each other. Although, as an observer, it is tempting
to attribute an apparent intentionality, there is nothing of the kind put in.
As a conclusion, we argue that emergent behavior may be much less rooted in explicit
intentions, internal motivations, or specific reward systems than is commonly
believed.'
article_number: '7846789'
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: 'Der R, Martius GS. Dynamical self consistency leads to behavioral development
and emergent social interactions in robots. In: IEEE; 2017. doi:10.1109/DEVLRN.2016.7846789'
apa: 'Der, R., & Martius, G. S. (2017). Dynamical self consistency leads to
behavioral development and emergent social interactions in robots. Presented at
the ICDL EpiRob: International Conference on Development and Learning and Epigenetic
Robotics , Cergy-Pontoise, France: IEEE. https://doi.org/10.1109/DEVLRN.2016.7846789'
chicago: Der, Ralf, and Georg S Martius. “Dynamical Self Consistency Leads to Behavioral
Development and Emergent Social Interactions in Robots.” IEEE, 2017. https://doi.org/10.1109/DEVLRN.2016.7846789.
ieee: 'R. Der and G. S. Martius, “Dynamical self consistency leads to behavioral
development and emergent social interactions in robots,” presented at the ICDL
EpiRob: International Conference on Development and Learning and Epigenetic Robotics
, Cergy-Pontoise, France, 2017.'
ista: 'Der R, Martius GS. 2017. Dynamical self consistency leads to behavioral development
and emergent social interactions in robots. ICDL EpiRob: International Conference
on Development and Learning and Epigenetic Robotics , 7846789.'
mla: Der, Ralf, and Georg S. Martius. Dynamical Self Consistency Leads to Behavioral
Development and Emergent Social Interactions in Robots. 7846789, IEEE, 2017,
doi:10.1109/DEVLRN.2016.7846789.
short: R. Der, G.S. Martius, in:, IEEE, 2017.
conference:
end_date: 2016-09-22
location: Cergy-Pontoise, France
name: 'ICDL EpiRob: International Conference on Development and Learning and Epigenetic
Robotics '
start_date: 2016-09-19
date_created: 2018-12-11T11:47:43Z
date_published: 2017-02-07T00:00:00Z
date_updated: 2021-01-12T08:07:51Z
day: '07'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/DEVLRN.2016.7846789
language:
- iso: eng
month: '02'
oa_version: None
publication_identifier:
isbn:
- 978-150905069-7
publication_status: published
publisher: IEEE
publist_id: '7100'
quality_controlled: '1'
scopus_import: 1
status: public
title: Dynamical self consistency leads to behavioral development and emergent social
interactions in robots
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '658'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, control becomes one
of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of specific
objectives for the task at hand. While very successful in many applications, self-organized
control schemes seem to be favored in large complex systems with unknown dynamics
or which are difficult to model. Reasons are the expected scalability, robustness,
and resilience of self-organizing systems. The paper presents a self-learning
neurocontroller based on extrinsic differential plasticity introduced recently,
applying it to an anthropomorphic musculoskeletal robot arm with attached objects
of unknown physical dynamics. The central finding of the paper is the following
effect: by the mere feedback through the internal dynamics of the object, the
robot is learning to relate each of the objects with a very specific sensorimotor
pattern. Specifically, an attached pendulum pilots the arm into a circular motion,
a half-filled bottle produces axis oriented shaking behavior, a wheel is getting
rotated, and wiping patterns emerge automatically in a table-plus-brush setting.
By these object-specific dynamical patterns, the robot may be said to recognize
the object''s identity, or in other words, it discovers dynamical affordances
of objects. Furthermore, when including hand coordinates obtained from a camera,
a dedicated hand-eye coordination self-organizes spontaneously. These phenomena
are discussed from a specific dynamical system perspective. Central is the dedicated
working regime at the border to instability with its potentially infinite reservoir
of (limit cycle) attractors "waiting" to be excited. Besides converging
toward one of these attractors, variate behavior is also arising from a self-induced
attractor morphing driven by the learning rule. We claim that experimental investigations
with this anthropomorphic, self-learning robot not only generate interesting and
potentially useful behaviors, but may also help to better understand what subjective
human muscle feelings are, how they can be rooted in sensorimotor patterns, and
how these concepts may feed back on robotics.'
article_number: '00008'
article_processing_charge: Yes
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Self organized behavior generation for musculoskeletal robots.
Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008
apa: Der, R., & Martius, G. S. (2017). Self organized behavior generation for
musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research
Foundation. https://doi.org/10.3389/fnbot.2017.00008
chicago: Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for
Musculoskeletal Robots.” Frontiers in Neurorobotics. Frontiers Research
Foundation, 2017. https://doi.org/10.3389/fnbot.2017.00008.
ieee: R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal
robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research
Foundation, 2017.
ista: Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal
robots. Frontiers in Neurorobotics. 11(MAR), 00008.
mla: Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal
Robots.” Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers
Research Foundation, 2017, doi:10.3389/fnbot.2017.00008.
short: R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).
date_created: 2018-12-11T11:47:45Z
date_published: 2017-03-16T00:00:00Z
date_updated: 2021-01-12T08:08:04Z
day: '16'
ddc:
- '006'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3389/fnbot.2017.00008
ec_funded: 1
file:
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checksum: b1bc43f96d1df3313c03032c2a46388d
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:18:49Z
date_updated: 2020-07-14T12:47:33Z
file_id: '5371'
file_name: IST-2017-903-v1+1_fnbot-11-00008.pdf
file_size: 8439566
relation: main_file
file_date_updated: 2020-07-14T12:47:33Z
has_accepted_license: '1'
intvolume: ' 11'
issue: MAR
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Frontiers in Neurorobotics
publication_identifier:
issn:
- '16625218'
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '7078'
pubrep_id: '903'
quality_controlled: '1'
scopus_import: 1
status: public
title: Self organized behavior generation for musculoskeletal robots
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2017'
...
---
_id: '6841'
abstract:
- lang: eng
text: In classical machine learning, regression is treated as a black box process
of identifying a suitable function from a hypothesis set without attempting to
gain insight into the mechanism connecting inputs and outputs. In the natural
sciences, however, finding an interpretable function for a phenomenon is the prime
goal as it allows to understand and generalize results. This paper proposes a
novel type of function learning network, called equation learner (EQL), that can
learn analytical expressions and is able to extrapolate to unseen domains. It
is implemented as an end-to-end differentiable feed-forward network and allows
for efficient gradient based training. Due to sparsity regularization concise
interpretable expressions can be obtained. Often the true underlying source expression
is identified.
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Martius GS, Lampert C. Extrapolation and learning equations. In: 5th International
Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
International Conference on Learning Representations; 2017.'
apa: 'Martius, G. S., & Lampert, C. (2017). Extrapolation and learning equations.
In 5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings. Toulon, France: International Conference on Learning Representations.'
chicago: Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.”
In 5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings. International Conference on Learning Representations, 2017.
ieee: G. S. Martius and C. Lampert, “Extrapolation and learning equations,” in 5th
International Conference on Learning Representations, ICLR 2017 - Workshop Track
Proceedings, Toulon, France, 2017.
ista: 'Martius GS, Lampert C. 2017. Extrapolation and learning equations. 5th International
Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
ICLR: International Conference on Learning Representations.'
mla: Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.”
5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings, International Conference on Learning Representations, 2017.
short: G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations,
ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations,
2017.
conference:
end_date: 2017-04-26
location: Toulon, France
name: 'ICLR: International Conference on Learning Representations'
start_date: 2017-04-24
date_created: 2019-09-01T22:01:00Z
date_published: 2017-02-21T00:00:00Z
date_updated: 2021-01-12T08:09:17Z
day: '21'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1610.02995'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1610.02995
month: '02'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: 5th International Conference on Learning Representations, ICLR 2017 -
Workshop Track Proceedings
publication_status: published
publisher: International Conference on Learning Representations
quality_controlled: '1'
scopus_import: 1
status: public
title: Extrapolation and learning equations
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '750'
abstract:
- lang: eng
text: Modern communication technologies allow first responders to contact thousands
of potential volunteers simultaneously for support during a crisis or disaster
event. However, such volunteer efforts must be well coordinated and monitored,
in order to offer an effective relief to the professionals. In this paper we extend
earlier work on optimally assigning volunteers to selected landmark locations.
In particular, we emphasize the aspect that obtaining good assignments requires
not only advanced computational tools, but also a realistic measure of distance
between volunteers and landmarks. Specifically, we propose the use of the Open
Street Map (OSM) driving distance instead of he previously used flight distance.
We find the OSM driving distance to be better aligned with the interests of volunteers
and first responders. Furthermore, we show that relying on the flying distance
leads to a substantial underestimation of the number of required volunteers, causing
negative side effects in case of an actual crisis situation.
author:
- first_name: Jasmin
full_name: Pielorz, Jasmin
id: 49BC895A-F248-11E8-B48F-1D18A9856A87
last_name: Pielorz
- first_name: Matthias
full_name: Prandtstetter, Matthias
last_name: Prandtstetter
- first_name: Markus
full_name: Straub, Markus
last_name: Straub
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pielorz J, Prandtstetter M, Straub M, Lampert C. Optimal geospatial volunteer
allocation needs realistic distances. In: 2017 IEEE International Conference
on Big Data. IEEE; 2017:3760-3763. doi:10.1109/BigData.2017.8258375'
apa: 'Pielorz, J., Prandtstetter, M., Straub, M., & Lampert, C. (2017). Optimal
geospatial volunteer allocation needs realistic distances. In 2017 IEEE International
Conference on Big Data (pp. 3760–3763). Boston, MA, United States: IEEE. https://doi.org/10.1109/BigData.2017.8258375'
chicago: Pielorz, Jasmin, Matthias Prandtstetter, Markus Straub, and Christoph Lampert.
“Optimal Geospatial Volunteer Allocation Needs Realistic Distances.” In 2017
IEEE International Conference on Big Data, 3760–63. IEEE, 2017. https://doi.org/10.1109/BigData.2017.8258375.
ieee: J. Pielorz, M. Prandtstetter, M. Straub, and C. Lampert, “Optimal geospatial
volunteer allocation needs realistic distances,” in 2017 IEEE International
Conference on Big Data, Boston, MA, United States, 2017, pp. 3760–3763.
ista: Pielorz J, Prandtstetter M, Straub M, Lampert C. 2017. Optimal geospatial
volunteer allocation needs realistic distances. 2017 IEEE International Conference
on Big Data. Big Data, 3760–3763.
mla: Pielorz, Jasmin, et al. “Optimal Geospatial Volunteer Allocation Needs Realistic
Distances.” 2017 IEEE International Conference on Big Data, IEEE, 2017,
pp. 3760–63, doi:10.1109/BigData.2017.8258375.
short: J. Pielorz, M. Prandtstetter, M. Straub, C. Lampert, in:, 2017 IEEE International
Conference on Big Data, IEEE, 2017, pp. 3760–3763.
conference:
end_date: 2017-12-14
location: Boston, MA, United States
name: Big Data
start_date: 2017-12-11
date_created: 2018-12-11T11:48:18Z
date_published: 2017-12-01T00:00:00Z
date_updated: 2021-01-12T08:13:55Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/BigData.2017.8258375
language:
- iso: eng
month: '12'
oa_version: None
page: 3760 - 3763
publication: 2017 IEEE International Conference on Big Data
publication_identifier:
isbn:
- 978-153862714-3
publication_status: published
publisher: IEEE
publist_id: '6906'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optimal geospatial volunteer allocation needs realistic distances
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '1000'
abstract:
- lang: eng
text: 'We study probabilistic models of natural images and extend the autoregressive
family of PixelCNN models by incorporating latent variables. Subsequently, we
describe two new generative image models that exploit different image transformations
as latent variables: a quantized grayscale view of the image or a multi-resolution
image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN
models: 1) their tendency to focus on low-level image details, while largely ignoring
high-level image information, such as object shapes, and 2) their computationally
costly procedure for image sampling. We experimentally demonstrate benefits of
our LatentPixelCNN models, in particular showing that they produce much more realistically
looking image samples than previous state-of-the-art probabilistic models. '
acknowledgement: We thank Tim Salimans for spotting a mistake in our preliminary arXiv
manuscript. This work was funded by the European Research Council under the European
Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036.
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural
image modeling. In: 34th International Conference on Machine Learning.
Vol 70. JMLR; 2017:1905-1914.'
apa: 'Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables
for natural image modeling. In 34th International Conference on Machine Learning
(Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary
Variables for Natural Image Modeling.” In 34th International Conference on
Machine Learning, 70:1905–14. JMLR, 2017.
ieee: A. Kolesnikov and C. Lampert, “PixelCNN models with auxiliary variables for
natural image modeling,” in 34th International Conference on Machine Learning,
Sydney, Australia, 2017, vol. 70, pp. 1905–1914.
ista: 'Kolesnikov A, Lampert C. 2017. PixelCNN models with auxiliary variables for
natural image modeling. 34th International Conference on Machine Learning. ICML:
International Conference on Machine Learning vol. 70, 1905–1914.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary
Variables for Natural Image Modeling.” 34th International Conference on Machine
Learning, vol. 70, JMLR, 2017, pp. 1905–14.
short: A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine
Learning, JMLR, 2017, pp. 1905–1914.
conference:
end_date: 2017-08-11
location: Sydney, Australia
name: 'ICML: International Conference on Machine Learning'
start_date: 2017-08-06
date_created: 2018-12-11T11:49:37Z
date_published: 2017-08-01T00:00:00Z
date_updated: 2023-09-22T09:50:41Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1612.08185'
isi:
- '000683309501102'
has_accepted_license: '1'
intvolume: ' 70'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1612.08185
month: '08'
oa: 1
oa_version: Submitted Version
page: 1905 - 1914
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: 34th International Conference on Machine Learning
publication_identifier:
isbn:
- 978-151085514-4
publication_status: published
publisher: JMLR
publist_id: '6398'
quality_controlled: '1'
scopus_import: '1'
status: public
title: PixelCNN models with auxiliary variables for natural image modeling
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 70
year: '2017'
...
---
_id: '998'
abstract:
- lang: eng
text: 'A major open problem on the road to artificial intelligence is the development
of incrementally learning systems that learn about more and more concepts over
time from a stream of data. In this work, we introduce a new training strategy,
iCaRL, that allows learning in such a class-incremental way: only the training
data for a small number of classes has to be present at the same time and new
classes can be added progressively. iCaRL learns strong classifiers and a data
representation simultaneously. This distinguishes it from earlier works that were
fundamentally limited to fixed data representations and therefore incompatible
with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet
ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period
of time where other strategies quickly fail. '
article_processing_charge: No
author:
- first_name: Sylvestre Alvise
full_name: Rebuffi, Sylvestre Alvise
last_name: Rebuffi
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier
and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587'
apa: 'Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL:
Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542).
Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
United States: IEEE. https://doi.org/10.1109/CVPR.2017.587'
chicago: 'Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph
Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42.
IEEE, 2017. https://doi.org/10.1109/CVPR.2017.587.'
ieee: 'S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental
classifier and representation learning,” presented at the CVPR: Computer Vision
and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.'
ista: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier
and representation learning. CVPR: Computer Vision and Pattern Recognition vol.
2017, 5533–5542.'
mla: 'Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation
Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.'
short: S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
conference:
end_date: 2017-07-26
location: Honolulu, HA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2017-07-21
date_created: 2018-12-11T11:49:37Z
date_published: 2017-04-14T00:00:00Z
date_updated: 2023-09-22T09:51:58Z
day: '14'
department:
- _id: ChLa
- _id: ChWo
doi: 10.1109/CVPR.2017.587
ec_funded: 1
external_id:
isi:
- '000418371405066'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1611.07725
month: '04'
oa: 1
oa_version: Submitted Version
page: 5533 - 5542
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
isbn:
- 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6400'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'iCaRL: Incremental classifier and representation learning'
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...
---
_id: '911'
abstract:
- lang: eng
text: We develop a probabilistic technique for colorizing grayscale natural images.
In light of the intrinsic uncertainty of this task, the proposed probabilistic
framework has numerous desirable properties. In particular, our model is able
to produce multiple plausible and vivid colorizations for a given grayscale image
and is one of the first colorization models to provide a proper stochastic sampling
scheme. Moreover, our training procedure is supported by a rigorous theoretical
framework that does not require any ad hoc heuristics and allows for efficient
modeling and learning of the joint pixel color distribution.We demonstrate strong
quantitative and qualitative experimental results on the CIFAR-10 dataset and
the challenging ILSVRC 2012 dataset.
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA
Press; 2017:85.1-85.12. doi:10.5244/c.31.85'
apa: 'Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization
(p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London,
United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85'
chicago: Royer, Amélie, Alexander Kolesnikov, and Christoph Lampert. “Probabilistic
Image Colorization,” 85.1-85.12. BMVA Press, 2017. https://doi.org/10.5244/c.31.85.
ieee: 'A. Royer, A. Kolesnikov, and C. Lampert, “Probabilistic image colorization,”
presented at the BMVC: British Machine Vision Conference, London, United Kingdom,
2017, p. 85.1-85.12.'
ista: 'Royer A, Kolesnikov A, Lampert C. 2017. Probabilistic image colorization.
BMVC: British Machine Vision Conference, 85.1-85.12.'
mla: Royer, Amélie, et al. Probabilistic Image Colorization. BMVA Press,
2017, p. 85.1-85.12, doi:10.5244/c.31.85.
short: A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.
conference:
end_date: 2017-09-07
location: London, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2017-09-04
date_created: 2018-12-11T11:49:09Z
date_published: 2017-09-01T00:00:00Z
date_updated: 2023-10-16T10:04:02Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.5244/c.31.85
ec_funded: 1
external_id:
arxiv:
- '1705.04258'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2020-08-10T07:14:33Z
date_updated: 2020-08-10T07:14:33Z
file_id: '8224'
file_name: 2017_BMVC_Royer.pdf
file_size: 1625363
relation: main_file
success: 1
file_date_updated: 2020-08-10T07:14:33Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 85.1-85.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
eisbn:
- 190172560X
publication_status: published
publisher: BMVA Press
publist_id: '6532'
quality_controlled: '1'
related_material:
record:
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Probabilistic image colorization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '1108'
abstract:
- lang: eng
text: In this work we study the learnability of stochastic processes with respect
to the conditional risk, i.e. the existence of a learning algorithm that improves
its next-step performance with the amount of observed data. We introduce a notion
of pairwise discrepancy between conditional distributions at different times steps
and show how certain properties of these discrepancies can be used to construct
a successful learning algorithm. Our main results are two theorems that establish
criteria for learnability for many classes of stochastic processes, including
all special cases studied previously in the literature.
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Alexander
full_name: Zimin, Alexander
id: 37099E9C-F248-11E8-B48F-1D18A9856A87
last_name: Zimin
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Zimin A, Lampert C. Learning theory for conditional risk minimization. In:
Vol 54. ML Research Press; 2017:213-222.'
apa: 'Zimin, A., & Lampert, C. (2017). Learning theory for conditional risk
minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence
and Statistics, Fort Lauderdale, FL, United States: ML Research Press.'
chicago: Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional
Risk Minimization,” 54:213–22. ML Research Press, 2017.
ieee: 'A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,”
presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale,
FL, United States, 2017, vol. 54, pp. 213–222.'
ista: 'Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization.
AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 213–222.'
mla: Zimin, Alexander, and Christoph Lampert. Learning Theory for Conditional
Risk Minimization. Vol. 54, ML Research Press, 2017, pp. 213–22.
short: A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.
conference:
end_date: 2017-04-22
location: Fort Lauderdale, FL, United States
name: 'AISTATS: Artificial Intelligence and Statistics'
start_date: 2017-04-20
date_created: 2018-12-11T11:50:11Z
date_published: 2017-04-01T00:00:00Z
date_updated: 2023-10-17T10:01:12Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
isi:
- '000509368500024'
intvolume: ' 54'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf
month: '04'
oa: 1
oa_version: Submitted Version
page: 213 - 222
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: ML Research Press
publist_id: '6261'
quality_controlled: '1'
status: public
title: Learning theory for conditional risk minimization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 54
year: '2017'
...
---
_id: '999'
abstract:
- lang: eng
text: 'In multi-task learning, a learner is given a collection of prediction tasks
and needs to solve all of them. In contrast to previous work, which required that
annotated training data must be available for all tasks, we consider a new setting,
in which for some tasks, potentially most of them, only unlabeled training data
is provided. Consequently, to solve all tasks, information must be transferred
between tasks with labels and tasks without labels. Focusing on an instance-based
transfer method we analyze two variants of this setting: when the set of labeled
tasks is fixed, and when it can be actively selected by the learner. We state
and prove a generalization bound that covers both scenarios and derive from it
an algorithm for making the choice of labeled tasks (in the active case) and for
transferring information between the tasks in a principled way. We also illustrate
the effectiveness of the algorithm on synthetic and real data. '
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks.
In: Vol 70. ML Research Press; 2017:2807-2816.'
apa: 'Pentina, A., & Lampert, C. (2017). Multi-task learning with labeled and
unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International
Conference on Machine Learning, Sydney, Australia: ML Research Press.'
chicago: Pentina, Anastasia, and Christoph Lampert. “Multi-Task Learning with Labeled
and Unlabeled Tasks,” 70:2807–16. ML Research Press, 2017.
ieee: 'A. Pentina and C. Lampert, “Multi-task learning with labeled and unlabeled
tasks,” presented at the ICML: International Conference on Machine Learning, Sydney,
Australia, 2017, vol. 70, pp. 2807–2816.'
ista: 'Pentina A, Lampert C. 2017. Multi-task learning with labeled and unlabeled
tasks. ICML: International Conference on Machine Learning, PMLR, vol. 70, 2807–2816.'
mla: Pentina, Anastasia, and Christoph Lampert. Multi-Task Learning with Labeled
and Unlabeled Tasks. Vol. 70, ML Research Press, 2017, pp. 2807–16.
short: A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.
conference:
end_date: 2017-08-11
location: Sydney, Australia
name: 'ICML: International Conference on Machine Learning'
start_date: 2017-08-06
date_created: 2018-12-11T11:49:37Z
date_published: 2017-06-08T00:00:00Z
date_updated: 2023-10-17T11:53:32Z
day: '08'
department:
- _id: ChLa
ec_funded: 1
external_id:
isi:
- '000683309502093'
intvolume: ' 70'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1602.06518
month: '06'
oa: 1
oa_version: Submitted Version
page: 2807 - 2816
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
isbn:
- '9781510855144'
publication_status: published
publisher: ML Research Press
publist_id: '6399'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Multi-task learning with labeled and unlabeled tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 70
year: '2017'
...
---
_id: '1098'
abstract:
- lang: eng
text: Better understanding of the potential benefits of information transfer and
representation learning is an important step towards the goal of building intelligent
systems that are able to persist in the world and learn over time. In this work,
we consider a setting where the learner encounters a stream of tasks but is able
to retain only limited information from each encountered task, such as a learned
predictor. In contrast to most previous works analyzing this scenario, we do not
make any distributional assumptions on the task generating process. Instead, we
formulate a complexity measure that captures the diversity of the observed tasks.
We provide a lifelong learning algorithm with error guarantees for every observed
task (rather than on average). We show sample complexity reductions in comparison
to solving every task in isolation in terms of our task complexity measure. Further,
our algorithmic framework can naturally be viewed as learning a representation
from encountered tasks with a neural network.
acknowledgement: "This work was in parts funded by the European Research Council under
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement
no 308036.\r\n\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Ruth
full_name: Urner, Ruth
last_name: Urner
citation:
ama: 'Pentina A, Urner R. Lifelong learning with weighted majority votes. In: Vol
29. Neural Information Processing Systems; 2016:3619-3627.'
apa: 'Pentina, A., & Urner, R. (2016). Lifelong learning with weighted majority
votes (Vol. 29, pp. 3619–3627). Presented at the NIPS: Neural Information Processing
Systems, Barcelona, Spain: Neural Information Processing Systems.'
chicago: Pentina, Anastasia, and Ruth Urner. “Lifelong Learning with Weighted Majority
Votes,” 29:3619–27. Neural Information Processing Systems, 2016.
ieee: 'A. Pentina and R. Urner, “Lifelong learning with weighted majority votes,”
presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain,
2016, vol. 29, pp. 3619–3627.'
ista: 'Pentina A, Urner R. 2016. Lifelong learning with weighted majority votes.
NIPS: Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 29, 3619–3627.'
mla: Pentina, Anastasia, and Ruth Urner. Lifelong Learning with Weighted Majority
Votes. Vol. 29, Neural Information Processing Systems, 2016, pp. 3619–27.
short: A. Pentina, R. Urner, in:, Neural Information Processing Systems, 2016, pp.
3619–3627.
conference:
end_date: 2016-12-10
location: Barcelona, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:08Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:15Z
day: '01'
ddc:
- '006'
department:
- _id: ChLa
ec_funded: 1
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:42Z
date_updated: 2018-12-12T10:12:42Z
file_id: '4961'
file_name: IST-2017-775-v1+1_main.pdf
file_size: 237111
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:43Z
date_updated: 2018-12-12T10:12:43Z
file_id: '4962'
file_name: IST-2017-775-v1+2_supplementary.pdf
file_size: 185818
relation: main_file
file_date_updated: 2018-12-12T10:12:43Z
has_accepted_license: '1'
intvolume: ' 29'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 3619-3627
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6277'
pubrep_id: '775'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lifelong learning with weighted majority votes
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1102'
abstract:
- lang: eng
text: Weakly-supervised object localization methods tend to fail for object classes
that consistently co-occur with the same background elements, e.g. trains on tracks.
We propose a method to overcome these failures by adding a very small amount of
model-specific additional annotation. The main idea is to cluster a deep network\'s
mid-level representations and assign object or distractor labels to each cluster.
Experiments show substantially improved localization results on the challenging
ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for
semantic segmentation.
acknowledgement: "This work was funded in parts by the European Research Council\r\nunder
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant\r\nagreement
no 308036. We gratefully acknowledge the support of NVIDIA Corporation with\r\nthe
donation of the GPUs used for this research."
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Improving weakly-supervised object localization by
micro-annotation. In: Proceedings of the British Machine Vision Conference
2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object
localization by micro-annotation. In Proceedings of the British Machine Vision
Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom:
BMVA Press. https://doi.org/10.5244/C.30.92'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” In Proceedings of the British Machine
Vision Conference 2016, 2016–September:92.1-92.12. BMVA Press, 2016. https://doi.org/10.5244/C.30.92.
ieee: A. Kolesnikov and C. Lampert, “Improving weakly-supervised object localization
by micro-annotation,” in Proceedings of the British Machine Vision Conference
2016, York, United Kingdom, 2016, vol. 2016–September, p. 92.1-92.12.
ista: 'Kolesnikov A, Lampert C. 2016. Improving weakly-supervised object localization
by micro-annotation. Proceedings of the British Machine Vision Conference 2016.
BMVC: British Machine Vision Conference vol. 2016–September, 92.1-92.12.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” Proceedings of the British Machine
Vision Conference 2016, vol. 2016–September, BMVA Press, 2016, p. 92.1-92.12,
doi:10.5244/C.30.92.
short: A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision
Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
conference:
end_date: 2016-09-22
location: York, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2016-09-19
date_created: 2018-12-11T11:50:09Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T06:48:18Z
day: '01'
department:
- _id: ChLa
doi: 10.5244/C.30.92
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.bmva.org/bmvc/2016/papers/paper092/paper092.pdf
month: '09'
oa: 1
oa_version: Published Version
page: 92.1-92.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2016
publication_status: published
publisher: BMVA Press
publist_id: '6273'
quality_controlled: '1'
scopus_import: 1
status: public
title: Improving weakly-supervised object localization by micro-annotation
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-September
year: '2016'
...
---
_id: '1214'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. While very successful with classical robots, these methods run into
severe difficulties when applied to soft robots, a new field of robotics with
large interest for human-robot interaction. We claim that a novel controller paradigm
opens new perspective for this field. This paper applies a recently developed
neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon
driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we
observe a vast variety of self-organized behavior patterns: when left alone, the
arm realizes pseudo-random sequences of different poses. By applying physical
forces, the system can be entrained into definite motion patterns like wiping
a table. Most interestingly, after attaching an object, the controller gets in
a functional resonance with the object''s internal dynamics, starting to shake
spontaneously bottles half-filled with water or sensitively driving an attached
pendulum into a circular mode. When attached to the crank of a wheel the neural
system independently develops to rotate it. In this way, the robot discovers affordances
of objects its body is interacting with.'
acknowledgement: RD thanks for the hospitality at the Max-Planck-Institute and for
helpful discussions with Nihat Ay and Keyan Zahedi.
article_number: '7759138'
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Raphael
full_name: Hostettler, Raphael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots:
Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November.
IEEE; 2016. doi:10.1109/IROS.2016.7759138'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant
control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on
Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138'
chicago: 'Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant
Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic
Arm,” Vol. 2016–November. IEEE, 2016. https://doi.org/10.1109/IROS.2016.7759138.'
ieee: 'G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for
soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented
at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS
, Daejeon, Korea, 2016, vol. 2016–November.'
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft
robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International
Conference on Intelligent Robots and Systems IROS vol. 2016–November, 7759138.'
mla: 'Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior
of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE,
2016, doi:10.1109/IROS.2016.7759138.'
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.
conference:
end_date: 2016-09-14
location: Daejeon, Korea
name: 'IEEE RSJ International Conference on Intelligent Robots and Systems IROS '
start_date: 2016-09-09
date_created: 2018-12-11T11:50:45Z
date_published: 2016-11-28T00:00:00Z
date_updated: 2021-01-12T06:49:08Z
day: '28'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/IROS.2016.7759138
language:
- iso: eng
month: '11'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '6121'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-November
year: '2016'
...
---
_id: '1369'
abstract:
- lang: eng
text: 'We introduce a new loss function for the weakly-supervised training of semantic
image segmentation models based on three guiding principles: to seed with weak
localization cues, to expand objects based on the information about which classes
can occur in an image, and to constrain the segmentations to coincide with object
boundaries. We show experimentally that training a deep convolutional neural network
using the proposed loss function leads to substantially better segmentations than
previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset.
We furthermore give insight into the working mechanism of our method by a detailed
experimental study that illustrates how the segmentation quality is affected by
each term of the proposed loss function as well as their combinations.'
alternative_title:
- LNCS
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for
weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three
principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711).
Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The
Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42'
chicago: 'Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation,” 9908:695–711. Springer,
2016. https://doi.org/10.1007/978-3-319-46493-0_42.'
ieee: 'A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles
for weakly-supervised image segmentation,” presented at the ECCV: European Conference
on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.'
ista: 'Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles
for weakly-supervised image segmentation. ECCV: European Conference on Computer
Vision, LNCS, vol. 9908, 695–711.'
mla: 'Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer,
2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.'
short: A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
conference:
end_date: 2016-10-14
location: Amsterdam, The Netherlands
name: 'ECCV: European Conference on Computer Vision'
start_date: 2016-10-11
date_created: 2018-12-11T11:51:37Z
date_published: 2016-09-15T00:00:00Z
date_updated: 2021-01-12T06:50:12Z
day: '15'
department:
- _id: ChLa
doi: 10.1007/978-3-319-46493-0_42
ec_funded: 1
intvolume: ' 9908'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1603.06098
month: '09'
oa: 1
oa_version: Preprint
page: 695 - 711
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5842'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Seed, expand and constrain: Three principles for weakly-supervised image segmentation'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 9908
year: '2016'
...
---
_id: '1707'
abstract:
- lang: eng
text: "Volunteer supporters play an important role in modern crisis and disaster
management. In the times of mobile Internet devices, help from thousands of volunteers
can be requested within a short time span, thus relieving professional helpers
from minor chores or geographically spread-out tasks. However, the simultaneous
availability of many volunteers also poses new problems. In particular, the volunteer
efforts must be well coordinated, or otherwise situations might emerge in which
too many idle volunteers at one location become more of a burden than a relief
to the professionals.\r\nIn this work, we study the task of optimally assigning
volunteers to selected locations, e.g. in order to perform regular measurements,
to report on damage, or to distribute information or resources to the population
in a crisis situation. We formulate the assignment tasks as an optimization problem
and propose an effective and efficient solution procedure. Experiments on real
data of the Team Österreich, consisting of over 36,000 Austrian volunteers, show
the effectiveness and efficiency of our approach."
acknowledgement: The DRIVER FP7 project has received funding from the European Unions
Seventh Framework Programme for research, technological development and demonstration
under grant agreement no 607798. RE-ACTA was funded within the framework of the
Austrian Security Research Programme KIRAS by the Federal Ministry for Transport,
Innovation and Technology.
article_number: '7402041'
author:
- first_name: Jasmin
full_name: Pielorz, Jasmin
id: 49BC895A-F248-11E8-B48F-1D18A9856A87
last_name: Pielorz
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pielorz J, Lampert C. Optimal geospatial allocation of volunteers for crisis
management. In: IEEE; 2016. doi:10.1109/ICT-DM.2015.7402041'
apa: 'Pielorz, J., & Lampert, C. (2016). Optimal geospatial allocation of volunteers
for crisis management. Presented at the ICT-DM: Information and Communication
Technologies for Disaster Management, Rennes, France: IEEE. https://doi.org/10.1109/ICT-DM.2015.7402041'
chicago: Pielorz, Jasmin, and Christoph Lampert. “Optimal Geospatial Allocation
of Volunteers for Crisis Management.” IEEE, 2016. https://doi.org/10.1109/ICT-DM.2015.7402041.
ieee: 'J. Pielorz and C. Lampert, “Optimal geospatial allocation of volunteers for
crisis management,” presented at the ICT-DM: Information and Communication Technologies
for Disaster Management, Rennes, France, 2016.'
ista: 'Pielorz J, Lampert C. 2016. Optimal geospatial allocation of volunteers for
crisis management. ICT-DM: Information and Communication Technologies for Disaster
Management, 7402041.'
mla: Pielorz, Jasmin, and Christoph Lampert. Optimal Geospatial Allocation of
Volunteers for Crisis Management. 7402041, IEEE, 2016, doi:10.1109/ICT-DM.2015.7402041.
short: J. Pielorz, C. Lampert, in:, IEEE, 2016.
conference:
end_date: 2015-12-02
location: Rennes, France
name: 'ICT-DM: Information and Communication Technologies for Disaster Management'
start_date: 2015-11-30
date_created: 2018-12-11T11:53:35Z
date_published: 2016-02-11T00:00:00Z
date_updated: 2021-01-12T06:52:39Z
day: '11'
department:
- _id: ChLa
doi: 10.1109/ICT-DM.2015.7402041
language:
- iso: eng
month: '02'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '5429'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optimal geospatial allocation of volunteers for crisis management
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2016'
...
---
_id: '8094'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. The idea is that the controller controls the world---the body plus
its environment---as reliably as possible. This paper focuses on new lines of
self-organization for developmental robotics. We apply the recently developed
differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder
system from the Myorobotics toolkit. In the experiments, we observe a vast variety
of self-organized behavior patterns: when left alone, the arm realizes pseudo-random
sequences of different poses. By applying physical forces, the system can be entrained
into definite motion patterns like wiping a table. Most interestingly, after attaching
an object, the controller gets in a functional resonance with the object''s internal
dynamics, starting to shake spontaneously bottles half-filled with water or sensitively
driving an attached pendulum into a circular mode. When attached to the crank
of a wheel the neural system independently discovers how to rotate it. In this
way, the robot discovers affordances of objects its body is interacting with.'
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Rafael
full_name: Hostettler, Rafael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon
driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial
Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized
control of an tendon driven arm by differential extrinsic plasticity. In Proceedings
of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico:
MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029'
chicago: Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized
Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In Proceedings
of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.
ieee: G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control
of an tendon driven arm by differential extrinsic plasticity,” in Proceedings
of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp.
142–143.
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of
an tendon driven arm by differential extrinsic plasticity. Proceedings of the
Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on
the Synthesis and Simulation of Living Systems vol. 28, 142–143.'
mla: Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by
Differential Extrinsic Plasticity.” Proceedings of the Artificial Life Conference
2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial
Life Conference 2016, MIT Press, 2016, pp. 142–143.
conference:
end_date: 2016-07-08
location: Cancun, Mexico
name: 'ALIFE 2016: 15th International Conference on the Synthesis and Simulation
of Living Systems'
start_date: 2016-07-04
date_created: 2020-07-05T22:00:47Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T08:16:53Z
day: '01'
ddc:
- '610'
department:
- _id: ChLa
- _id: GaTk
doi: 10.7551/978-0-262-33936-0-ch029
ec_funded: 1
file:
- access_level: open_access
checksum: cff63e7a4b8ac466ba51a9c84153a940
content_type: application/pdf
creator: cziletti
date_created: 2020-07-06T12:59:09Z
date_updated: 2020-07-14T12:48:09Z
file_id: '8096'
file_name: 2016_ProcALIFE_Martius.pdf
file_size: 678670
relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 142-143
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the Artificial Life Conference 2016
publication_identifier:
isbn:
- '9780262339360'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: 1
status: public
title: Self-organized control of an tendon driven arm by differential extrinsic plasticity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 28
year: '2016'
...
---
_id: '1126'
abstract:
- lang: eng
text: "Traditionally machine learning has been focusing on the problem of solving
a single\r\ntask in isolation. While being quite well understood, this approach
disregards an\r\nimportant aspect of human learning: when facing a new problem,
humans are able to\r\nexploit knowledge acquired from previously learned tasks.
Intuitively, access to several\r\nproblems simultaneously or sequentially could
also be advantageous for a machine\r\nlearning system, especially if these tasks
are closely related. Indeed, results of many\r\nempirical studies have provided
justification for this intuition. However, theoretical\r\njustifications of this
idea are rather limited.\r\nThe focus of this thesis is to expand the understanding
of potential benefits of information\r\ntransfer between several related learning
problems. We provide theoretical\r\nanalysis for three scenarios of multi-task
learning - multiple kernel learning, sequential\r\nlearning and active task selection.
We also provide a PAC-Bayesian perspective on\r\nlifelong learning and investigate
how the task generation process influences the generalization\r\nguarantees in
this scenario. In addition, we show how some of the obtained\r\ntheoretical results
can be used to derive principled multi-task and lifelong learning\r\nalgorithms
and illustrate their performance on various synthetic and real-world datasets."
acknowledgement: "First and foremost I would like to express my gratitude to my supervisor,
Christoph\r\nLampert. Thank you for your patience in teaching me all aspects of
doing research\r\n(including English grammar), for your trust in my capabilities
and endless support. Thank\r\nyou for granting me freedom in my research and, at
the same time, having time and\r\nhelping me cope with the consequences whenever
I needed it. Thank you for creating\r\nan excellent atmosphere in the group, it
was a great pleasure and honor to be a part of\r\nit. There could not have been
a better and more inspiring adviser and mentor.\r\nI thank Shai Ben-David for welcoming
me into his group at the University of Waterloo,\r\nfor inspiring discussions and
support. It was a great pleasure to work together. I am\r\nalso thankful to Ruth
Urner for hosting me at the Max-Planck Institute Tübingen, for the\r\nfruitful
collaboration and for taking care of me during that not-so-sunny month of May.\r\nI
thank Jan Maas for kindly joining my thesis committee despite the short notice and\r\nproviding
me with insightful comments.\r\nI would like to thank my colleagues for their support,
entertaining conversations and\r\nendless table soccer games we shared together:
Georg, Jan, Amelie and Emilie, Michal\r\nand Alex, Alex K. and Alex Z., Thomas,
Sameh, Vlad, Mayu, Nathaniel, Silvester, Neel,\r\nCsaba, Vladimir, Morten. Thank
you, Mabel and Ram, for the wonderful time we spent\r\ntogether. I am thankful to
Shrinu and Samira for taking care of me during my stay at the\r\nUniversity of Waterloo.
Special thanks to Viktoriia for her never-ending optimism and for\r\nbeing so inspiring
and supportive, especially at the beginning of my PhD journey.\r\nThanks to IST
administration, in particular, Vlad and Elisabeth for shielding me from\r\nmost
of the bureaucratic paperwork.\r\n\r\nThis dissertation would not have been possible
without funding from the European\r\nResearch Council under the European Union's
Seventh Framework Programme\r\n(FP7/2007-2013)/ERC grant agreement no 308036."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
citation:
ama: Pentina A. Theoretical foundations of multi-task lifelong learning. 2016. doi:10.15479/AT:ISTA:TH_776
apa: Pentina, A. (2016). Theoretical foundations of multi-task lifelong learning.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH_776
chicago: Pentina, Anastasia. “Theoretical Foundations of Multi-Task Lifelong Learning.”
Institute of Science and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:TH_776.
ieee: A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute
of Science and Technology Austria, 2016.
ista: Pentina A. 2016. Theoretical foundations of multi-task lifelong learning.
Institute of Science and Technology Austria.
mla: Pentina, Anastasia. Theoretical Foundations of Multi-Task Lifelong Learning.
Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:TH_776.
short: A. Pentina, Theoretical Foundations of Multi-Task Lifelong Learning, Institute
of Science and Technology Austria, 2016.
date_created: 2018-12-11T11:50:17Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2023-09-07T11:52:03Z
day: '01'
ddc:
- '006'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH_776
ec_funded: 1
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:07Z
date_updated: 2018-12-12T10:14:07Z
file_id: '5056'
file_name: IST-2017-776-v1+1_Pentina_Thesis_2016.pdf
file_size: 2140062
relation: main_file
file_date_updated: 2018-12-12T10:14:07Z
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: '127'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6234'
pubrep_id: '776'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Theoretical foundations of multi-task lifelong learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2016'
...
---
_id: '1425'
abstract:
- lang: eng
text: 'In this work we aim at extending the theoretical foundations of lifelong
learning. Previous work analyzing this scenario is based on the assumption that
learning tasks are sampled i.i.d. from a task environment or limited to strongly
constrained data distributions. Instead, we study two scenarios when lifelong
learning is possible, even though the observed tasks do not form an i.i.d. sample:
first, when they are sampled from the same environment, but possibly with dependencies,
and second, when the task environment is allowed to change over time in a consistent
way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct
generalization of the analogous previous result for the i.i.d. case. For the second
scenario we propose to learn an inductive bias in form of a transfer procedure.
We present a generalization bound and show on a toy example how it can be used
to identify a beneficial transfer algorithm.'
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. Lifelong learning with non-i.i.d. tasks. In: Vol 2015.
Neural Information Processing Systems; 2015:1540-1548.'
apa: 'Pentina, A., & Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks
(Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing
Systems, Montreal, Canada: Neural Information Processing Systems.'
chicago: Pentina, Anastasia, and Christoph Lampert. “Lifelong Learning with Non-i.i.d.
Tasks,” 2015:1540–48. Neural Information Processing Systems, 2015.
ieee: 'A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented
at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol.
2015, pp. 1540–1548.'
ista: 'Pentina A, Lampert C. 2015. Lifelong learning with non-i.i.d. tasks. NIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 2015, 1540–1548.'
mla: Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d.
Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.
short: A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015,
pp. 1540–1548.
conference:
end_date: 2015-12-12
location: Montreal, Canada
name: 'NIPS: Neural Information Processing Systems'
start_date: 2015-12-07
date_created: 2018-12-11T11:51:57Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:50:39Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
intvolume: ' 2015'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.nips.cc/paper/6007-lifelong-learning-with-non-iid-tasks
month: '01'
oa: 1
oa_version: None
page: 1540 - 1548
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5781'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lifelong learning with non-i.i.d. tasks
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2015
year: '2015'
...
---
_id: '1533'
abstract:
- lang: eng
text: This paper addresses the problem of semantic segmentation, where the possible
class labels are from a predefined set. We exploit top-down guidance, i.e., the
coarse localization of the objects and their class labels provided by object detectors.
For each detected bounding box, figure-ground segmentation is performed and the
final result is achieved by merging the figure-ground segmentations. The main
idea of the proposed approach, which is presented in our preliminary work, is
to reformulate the figure-ground segmentation problem as sparse reconstruction
pursuing the object mask in a nonparametric manner. The latent segmentation mask
should be coherent subject to sparse error caused by intra-category diversity;
thus, the object mask is inferred by making use of sparse representations over
the training set. To handle local spatial deformations, local patch-level masks
are also considered and inferred by sparse representations over the spatially
nearby patches. The sparse reconstruction coefficients and the latent mask are
alternately optimized by applying the Lasso algorithm and the accelerated proximal
gradient method. The proposed formulation results in a convex optimization problem;
thus, the global optimal solution is achieved. In this paper, we provide theoretical
analysis of the convergence and optimality. We also give an extended numerical
analysis of the proposed algorithm and a comprehensive comparison with the related
semantic segmentation methods on the challenging PASCAL visual object class object
segmentation datasets and the Weizmann horse dataset. The experimental results
demonstrate that the proposed algorithm achieves a competitive performance when
compared with the state of the arts.
author:
- first_name: Wei
full_name: Xia, Wei
last_name: Xia
- first_name: Csaba
full_name: Domokos, Csaba
id: 492DACF8-F248-11E8-B48F-1D18A9856A87
last_name: Domokos
- first_name: Junjun
full_name: Xiong, Junjun
last_name: Xiong
- first_name: Loongfah
full_name: Cheong, Loongfah
last_name: Cheong
- first_name: Shuicheng
full_name: Yan, Shuicheng
last_name: Yan
citation:
ama: Xia W, Domokos C, Xiong J, Cheong L, Yan S. Segmentation over detection via
optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for
Video Technology. 2015;25(8):1295-1308. doi:10.1109/TCSVT.2014.2379972
apa: Xia, W., Domokos, C., Xiong, J., Cheong, L., & Yan, S. (2015). Segmentation
over detection via optimal sparse reconstructions. IEEE Transactions on Circuits
and Systems for Video Technology. IEEE. https://doi.org/10.1109/TCSVT.2014.2379972
chicago: Xia, Wei, Csaba Domokos, Junjun Xiong, Loongfah Cheong, and Shuicheng Yan.
“Segmentation over Detection via Optimal Sparse Reconstructions.” IEEE Transactions
on Circuits and Systems for Video Technology. IEEE, 2015. https://doi.org/10.1109/TCSVT.2014.2379972.
ieee: W. Xia, C. Domokos, J. Xiong, L. Cheong, and S. Yan, “Segmentation over detection
via optimal sparse reconstructions,” IEEE Transactions on Circuits and Systems
for Video Technology, vol. 25, no. 8. IEEE, pp. 1295–1308, 2015.
ista: Xia W, Domokos C, Xiong J, Cheong L, Yan S. 2015. Segmentation over detection
via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems
for Video Technology. 25(8), 1295–1308.
mla: Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”
IEEE Transactions on Circuits and Systems for Video Technology, vol. 25,
no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.
short: W. Xia, C. Domokos, J. Xiong, L. Cheong, S. Yan, IEEE Transactions on Circuits
and Systems for Video Technology 25 (2015) 1295–1308.
date_created: 2018-12-11T11:52:34Z
date_published: 2015-08-01T00:00:00Z
date_updated: 2021-01-12T06:51:26Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/TCSVT.2014.2379972
intvolume: ' 25'
issue: '8'
language:
- iso: eng
month: '08'
oa_version: None
page: 1295 - 1308
publication: IEEE Transactions on Circuits and Systems for Video Technology
publication_status: published
publisher: IEEE
publist_id: '5638'
quality_controlled: '1'
scopus_import: 1
status: public
title: Segmentation over detection via optimal sparse reconstructions
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2015'
...
---
_id: '1570'
abstract:
- lang: eng
text: Grounding autonomous behavior in the nervous system is a fundamental challenge
for neuroscience. In particular, self-organized behavioral development provides
more questions than answers. Are there special functional units for curiosity,
motivation, and creativity? This paper argues that these features can be grounded
in synaptic plasticity itself, without requiring any higher-level constructs.
We propose differential extrinsic plasticity (DEP) as a new synaptic rule for
self-learning systems and apply it to a number of complex robotic systems as a
test case. Without specifying any purpose or goal, seemingly purposeful and adaptive
rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
These surprising results require no systemspecific modifications of the DEP rule.
They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
is due to the tight brain body environment coupling. The new synaptic rule is
biologically plausible and would be an interesting target for neurobiological
investigation. We also argue that this neuronal mechanism may have been a catalyst
in natural evolution.
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112
apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the
development of sensorimotor intelligence. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1508400112
chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1508400112.
ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy
of Sciences, pp. E6224–E6232, 2015.
ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy
of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.
short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2021-01-12T06:51:40Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
pmid:
- '26504200'
intvolume: ' 112'
issue: '45'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: 1
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '1706'
abstract:
- lang: eng
text: We consider a problem of learning kernels for use in SVM classification in
the multi-task and lifelong scenarios and provide generalization bounds on the
error of a large margin classifier. Our results show that, under mild conditions
on the family of kernels used for learning, solving several related tasks simultaneously
is beneficial over single task learning. In particular, as the number of observed
tasks grows, assuming that in the considered family of kernels there exists one
that yields low approximation error on all tasks, the overhead associated with
learning such a kernel vanishes and the complexity converges to that of learning
when this good kernel is given to the learner.
alternative_title:
- LNCS
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Shai
full_name: Ben David, Shai
last_name: Ben David
citation:
ama: 'Pentina A, Ben David S. Multi-task and lifelong learning of kernels. In: Vol
9355. Springer; 2015:194-208. doi:10.1007/978-3-319-24486-0_13'
apa: 'Pentina, A., & Ben David, S. (2015). Multi-task and lifelong learning
of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning
Theory, Banff, AB, Canada: Springer. https://doi.org/10.1007/978-3-319-24486-0_13'
chicago: Pentina, Anastasia, and Shai Ben David. “Multi-Task and Lifelong Learning
of Kernels,” 9355:194–208. Springer, 2015. https://doi.org/10.1007/978-3-319-24486-0_13.
ieee: 'A. Pentina and S. Ben David, “Multi-task and lifelong learning of kernels,”
presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada, 2015, vol.
9355, pp. 194–208.'
ista: 'Pentina A, Ben David S. 2015. Multi-task and lifelong learning of kernels.
ALT: Algorithmic Learning Theory, LNCS, vol. 9355, 194–208.'
mla: Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning
of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.
short: A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208.
conference:
end_date: 2015-10-06
location: Banff, AB, Canada
name: 'ALT: Algorithmic Learning Theory'
start_date: 2015-10-04
date_created: 2018-12-11T11:53:35Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:52:39Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-24486-0_13
ec_funded: 1
intvolume: ' 9355'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1602.06531
month: '01'
oa: 1
oa_version: Preprint
page: 194 - 208
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5430'
quality_controlled: '1'
scopus_import: 1
status: public
title: Multi-task and lifelong learning of kernels
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9355
year: '2015'
...
---
_id: '1859'
abstract:
- lang: eng
text: "Structural support vector machines (SSVMs) are amongst the best performing
models for structured computer vision tasks, such as semantic image segmentation
or human pose estimation. Training SSVMs, however, is computationally costly,
because it requires repeated calls to a structured prediction subroutine (called
\\emph{max-oracle}), which has to solve an optimization problem itself, e.g. a
graph cut.\r\nIn this work, we introduce a new algorithm for SSVM training that
is more efficient than earlier techniques when the max-oracle is computationally
expensive, as it is frequently the case in computer vision tasks. The main idea
is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm
with efficient hyperplane caching, and (ii) use an automatic selection rule for
deciding whether to call the exact max-oracle or to rely on an approximate one
based on the cached hyperplanes.\r\nWe show experimentally that this strategy
leads to faster convergence to the optimum with respect to the number of requires
oracle calls, and that this translates into faster convergence with respect to
the total runtime when the max-oracle is slow compared to the other steps of the
algorithm. "
author:
- first_name: Neel
full_name: Shah, Neel
id: 31ABAF80-F248-11E8-B48F-1D18A9856A87
last_name: Shah
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Shah N, Kolmogorov V, Lampert C. A multi-plane block-coordinate Frank-Wolfe
algorithm for training structural SVMs with a costly max-oracle. In: IEEE; 2015:2737-2745.
doi:10.1109/CVPR.2015.7298890'
apa: 'Shah, N., Kolmogorov, V., & Lampert, C. (2015). A multi-plane block-coordinate
Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp.
2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston,
MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7298890'
chicago: Shah, Neel, Vladimir Kolmogorov, and Christoph Lampert. “A Multi-Plane
Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly
Max-Oracle,” 2737–45. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298890.
ieee: 'N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe
algorithm for training structural SVMs with a costly max-oracle,” presented at
the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp.
2737–2745.'
ista: 'Shah N, Kolmogorov V, Lampert C. 2015. A multi-plane block-coordinate Frank-Wolfe
algorithm for training structural SVMs with a costly max-oracle. CVPR: Computer
Vision and Pattern Recognition, 2737–2745.'
mla: Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm
for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45,
doi:10.1109/CVPR.2015.7298890.
short: N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.
conference:
end_date: 2015-06-12
location: Boston, MA, USA
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:24Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2021-01-12T06:53:40Z
day: '01'
department:
- _id: VlKo
- _id: ChLa
doi: 10.1109/CVPR.2015.7298890
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1408.6804
month: '06'
oa: 1
oa_version: Preprint
page: 2737 - 2745
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_status: published
publisher: IEEE
publist_id: '5240'
quality_controlled: '1'
scopus_import: 1
status: public
title: A multi-plane block-coordinate Frank-Wolfe algorithm for training structural
SVMs with a costly max-oracle
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1860'
abstract:
- lang: eng
text: Classifiers for object categorization are usually evaluated by their accuracy
on a set of i.i.d. test examples. This provides us with an estimate of the expected
error when applying the classifiers to a single new image. In real application,
however, classifiers are rarely only used for a single image and then discarded.
Instead, they are applied sequentially to many images, and these are typically
not i.i.d. samples from a fixed data distribution, but they carry dependencies
and their class distribution varies over time. In this work, we argue that the
phenomenon of correlated data at prediction time is not a nuisance, but a blessing
in disguise. We describe a probabilistic method for adapting classifiers at prediction
time without having to retrain them. We also introduce a framework for creating
realistically distributed image sequences, which offers a way to benchmark classifier
adaptation methods, such as the one we propose. Experiments on the ILSVRC2010
and ILSVRC2012 datasets show that adapting object classification systems at prediction
time can significantly reduce their error rate, even with no additional human
feedback.
author:
- first_name: Amélie
full_name: Royer, Amélie
last_name: Royer
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. Classifier adaptation at prediction time. In: IEEE; 2015:1401-1409.
doi:10.1109/CVPR.2015.7298746'
apa: 'Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time
(pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition,
Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746'
chicago: Royer, Amélie, and Christoph Lampert. “Classifier Adaptation at Prediction
Time,” 1401–9. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298746.
ieee: 'A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented
at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States,
2015, pp. 1401–1409.'
ista: 'Royer A, Lampert C. 2015. Classifier adaptation at prediction time. CVPR:
Computer Vision and Pattern Recognition, 1401–1409.'
mla: Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction
Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.
short: A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.
conference:
end_date: 2015-06-12
location: Boston, MA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:24Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2021-01-12T06:53:41Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7298746
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Royer_Classifier_Adaptation_at_2015_CVPR_paper.pdf
month: '06'
oa: 1
oa_version: Submitted Version
page: 1401 - 1409
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '5239'
quality_controlled: '1'
scopus_import: 1
status: public
title: Classifier adaptation at prediction time
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1858'
abstract:
- lang: eng
text: 'We study the problem of predicting the future, though only in the probabilistic
sense of estimating a future state of a time-varying probability distribution.
This is not only an interesting academic problem, but solving this extrapolation
problem also has many practical application, e.g. for training classifiers that
have to operate under time-varying conditions. Our main contribution is a method
for predicting the next step of the time-varying distribution from a given sequence
of sample sets from earlier time steps. For this we rely on two recent machine
learning techniques: embedding probability distributions into a reproducing kernel
Hilbert space, and learning operators by vector-valued regression. We illustrate
the working principles and the practical usefulness of our method by experiments
on synthetic and real data. We also highlight an exemplary application: training
a classifier in a domain adaptation setting without having access to examples
from the test time distribution at training time.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Predicting the future behavior of a time-varying probability distribution.
In: IEEE; 2015:942-950. doi:10.1109/CVPR.2015.7298696'
apa: 'Lampert, C. (2015). Predicting the future behavior of a time-varying probability
distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern
Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298696'
chicago: Lampert, Christoph. “Predicting the Future Behavior of a Time-Varying Probability
Distribution,” 942–50. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298696.
ieee: 'C. Lampert, “Predicting the future behavior of a time-varying probability
distribution,” presented at the CVPR: Computer Vision and Pattern Recognition,
Boston, MA, United States, 2015, pp. 942–950.'
ista: 'Lampert C. 2015. Predicting the future behavior of a time-varying probability
distribution. CVPR: Computer Vision and Pattern Recognition, 942–950.'
mla: Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability
Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.
short: C. Lampert, in:, IEEE, 2015, pp. 942–950.
conference:
end_date: 2015-06-12
location: Boston, MA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:24Z
date_published: 2015-10-15T00:00:00Z
date_updated: 2021-01-12T06:53:40Z
day: '15'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7298696
external_id:
arxiv:
- '1406.5362'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1406.5362
month: '10'
oa: 1
oa_version: Preprint
page: 942 - 950
publication_status: published
publisher: IEEE
publist_id: '5241'
quality_controlled: '1'
scopus_import: 1
status: public
title: Predicting the future behavior of a time-varying probability distribution
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1857'
abstract:
- lang: eng
text: 'Sharing information between multiple tasks enables algorithms to achieve
good generalization performance even from small amounts of training data. However,
in a realistic scenario of multi-task learning not all tasks are equally related
to each other, hence it could be advantageous to transfer information only between
the most related tasks. In this work we propose an approach that processes multiple
tasks in a sequence with sharing between subsequent tasks instead of solving all
tasks jointly. Subsequently, we address the question of curriculum learning of
tasks, i.e. finding the best order of tasks to be learned. Our approach is based
on a generalization bound criterion for choosing the task order that optimizes
the average expected classification performance over all tasks. Our experimental
results show that learning multiple related tasks sequentially can be more effective
than learning them jointly, the order in which tasks are being solved affects
the overall performance, and that our model is able to automatically discover
the favourable order of tasks. '
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Sharmanska V, Lampert C. Curriculum learning of multiple tasks.
In: IEEE; 2015:5492-5500. doi:10.1109/CVPR.2015.7299188'
apa: 'Pentina, A., Sharmanska, V., & Lampert, C. (2015). Curriculum learning
of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and
Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7299188'
chicago: Pentina, Anastasia, Viktoriia Sharmanska, and Christoph Lampert. “Curriculum
Learning of Multiple Tasks,” 5492–5500. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7299188.
ieee: 'A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple
tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston,
MA, United States, 2015, pp. 5492–5500.'
ista: 'Pentina A, Sharmanska V, Lampert C. 2015. Curriculum learning of multiple
tasks. CVPR: Computer Vision and Pattern Recognition, 5492–5500.'
mla: Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE,
2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188.
short: A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.
conference:
end_date: 2015-06-12
location: Boston, MA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:23Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2023-02-23T10:17:31Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7299188
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1412.1353
month: '06'
oa: 1
oa_version: Preprint
page: 5492 - 5500
publication_status: published
publisher: IEEE
publist_id: '5243'
quality_controlled: '1'
scopus_import: 1
status: public
title: Curriculum learning of multiple tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '12881'
acknowledgement: This work was supported by the DFG (SPP 1527) and the EU (FP7, REA
grant no 291734).
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Eckehard
full_name: Olbrich, Eckehard
last_name: Olbrich
citation:
ama: 'Martius GS, Olbrich E. Quantifying self-organizing behavior of autonomous
robots. In: Proceedings of the 13th European Conference on Artificial Life.
MIT Press; 2015:78. doi:10.7551/978-0-262-33027-5-ch018'
apa: 'Martius, G. S., & Olbrich, E. (2015). Quantifying self-organizing behavior
of autonomous robots. In Proceedings of the 13th European Conference on Artificial
Life (p. 78). York, United Kingdom: MIT Press. https://doi.org/10.7551/978-0-262-33027-5-ch018'
chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Self-Organizing Behavior
of Autonomous Robots.” In Proceedings of the 13th European Conference on Artificial
Life, 78. MIT Press, 2015. https://doi.org/10.7551/978-0-262-33027-5-ch018.
ieee: G. S. Martius and E. Olbrich, “Quantifying self-organizing behavior of autonomous
robots,” in Proceedings of the 13th European Conference on Artificial Life,
York, United Kingdom, 2015, p. 78.
ista: 'Martius GS, Olbrich E. 2015. Quantifying self-organizing behavior of autonomous
robots. Proceedings of the 13th European Conference on Artificial Life. ECAL:
European Conference on Artificial Life, 78.'
mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior
of Autonomous Robots.” Proceedings of the 13th European Conference on Artificial
Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018.
short: G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference
on Artificial Life, MIT Press, 2015, p. 78.
conference:
end_date: 2015-07-24
location: York, United Kingdom
name: 'ECAL: European Conference on Artificial Life'
start_date: 2015-07-20
date_created: 2023-04-30T22:01:07Z
date_published: 2015-07-01T00:00:00Z
date_updated: 2023-05-02T07:06:21Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.7551/978-0-262-33027-5-ch018
ec_funded: 1
file:
- access_level: open_access
checksum: 880eabe59c9df12f06a882aa1bc4e600
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:02:59Z
date_updated: 2023-05-02T07:02:59Z
file_id: '12882'
file_name: 2015_ECAL_Martius.pdf
file_size: 1674241
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:02:59Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '78'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 13th European Conference on Artificial Life
publication_identifier:
isbn:
- '9780262330275'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying self-organizing behavior of autonomous robots
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1401'
abstract:
- lang: eng
text: 'The human ability to recognize objects in complex scenes has driven research
in the computer vision field over couple of decades. This thesis focuses on the
object recognition task in images. That is, given the image, we want the computer
system to be able to predict the class of the object that appears in the image.
A recent successful attempt to bridge semantic understanding of the image perceived
by humans and by computers uses attribute-based models. Attributes are semantic
properties of the objects shared across different categories, which humans and
computers can decide on. To explore the attribute-based models we take a statistical
machine learning approach, and address two key learning challenges in view of
object recognition task: learning augmented attributes as mid-level discriminative
feature representation, and learning with attributes as privileged information.
Our main contributions are parametric and non-parametric models and algorithms
to solve these frameworks. In the parametric approach, we explore an autoencoder
model combined with the large margin nearest neighbor principle for mid-level
feature learning, and linear support vector machines for learning with privileged
information. In the non-parametric approach, we propose a supervised Indian Buffet
Process for automatic augmentation of semantic attributes, and explore the Gaussian
Processes classification framework for learning with privileged information. A
thorough experimental analysis shows the effectiveness of the proposed models
in both parametric and non-parametric views.'
acknowledgement: "I would like to thank my supervisor, Christoph Lampert, for guidance
throughout my studies and for patience in transforming me into a scientist, and
my thesis committee, Chris Wojtan and Horst Bischof, for their help and advice.
\r\n\r\nI would like to thank Elisabeth Hacker who perfectly assisted all my administrative
needs and was always nice and friendly to me, and the campus team for making the
IST Austria campus my second home. \r\nI was honored to collaborate with brilliant
researchers and to learn from their experience. Undoubtedly, I learned most of all
from Novi Quadrianto: brainstorming our projects and getting exciting results was
the most enjoyable part of my work – thank you! I am also grateful to David Knowles,
Zoubin Ghahramani, Daniel Hernández-Lobato, Kristian Kersting and Anastasia Pentina
for the fantastic projects we worked on together, and to Kristen Grauman and Adriana
Kovashka for the exceptional experience working with user studies. I would like
to thank my colleagues at IST Austria and my office mates who shared their happy
moods, scientific breakthroughs and thought-provoking conversations with me: Chao,
Filip, Rustem, Asya, Sameh, Alex, Vlad, Mayu, Neel, Csaba, Thomas, Vladimir, Cristina,
Alex Z., Avro, Amelie and Emilie, Andreas H. and Andreas E., Chris, Lena, Michael,
Ali and Ipek, Vera, Igor, Katia. Special thanks to Morten for the countless games
of table soccer we played together and the tournaments we teamed up for: we will
definitely win next time:) A very warm hug to Asya for always being so inspiring
and supportive to me, and for helping me to increase the proportion of female computer
scientists in our group. "
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
citation:
ama: 'Sharmanska V. Learning with attributes for object recognition: Parametric
and non-parametrics views. 2015. doi:10.15479/at:ista:1401'
apa: 'Sharmanska, V. (2015). Learning with attributes for object recognition:
Parametric and non-parametrics views. Institute of Science and Technology
Austria. https://doi.org/10.15479/at:ista:1401'
chicago: 'Sharmanska, Viktoriia. “Learning with Attributes for Object Recognition:
Parametric and Non-Parametrics Views.” Institute of Science and Technology Austria,
2015. https://doi.org/10.15479/at:ista:1401.'
ieee: 'V. Sharmanska, “Learning with attributes for object recognition: Parametric
and non-parametrics views,” Institute of Science and Technology Austria, 2015.'
ista: 'Sharmanska V. 2015. Learning with attributes for object recognition: Parametric
and non-parametrics views. Institute of Science and Technology Austria.'
mla: 'Sharmanska, Viktoriia. Learning with Attributes for Object Recognition:
Parametric and Non-Parametrics Views. Institute of Science and Technology
Austria, 2015, doi:10.15479/at:ista:1401.'
short: 'V. Sharmanska, Learning with Attributes for Object Recognition: Parametric
and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.'
date_created: 2018-12-11T11:51:48Z
date_published: 2015-04-01T00:00:00Z
date_updated: 2023-09-07T11:40:11Z
day: '01'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
- _id: GradSch
doi: 10.15479/at:ista:1401
file:
- access_level: open_access
checksum: 3605b402bb6934e09ae4cf672c84baf7
content_type: application/pdf
creator: dernst
date_created: 2021-02-22T11:33:17Z
date_updated: 2021-02-22T11:33:17Z
file_id: '9177'
file_name: 2015_Thesis_Sharmanska.pdf
file_size: 7964342
relation: main_file
success: 1
- access_level: closed
checksum: e37593b3ee75bf3180629df2d6ca8f4e
content_type: application/pdf
creator: cchlebak
date_created: 2021-11-16T14:40:45Z
date_updated: 2021-11-17T13:47:24Z
file_id: '10297'
file_name: 2015_Thesis_Sharmanska_pdfa.pdf
file_size: 7372241
relation: main_file
file_date_updated: 2021-11-17T13:47:24Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- url: http://users.sussex.ac.uk/~nq28/viktoriia/Thesis_Sharmanska.pdf
month: '04'
oa: 1
oa_version: Published Version
page: '144'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '5806'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: 'Learning with attributes for object recognition: Parametric and non-parametrics
views'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2015'
...
---
_id: '1655'
abstract:
- lang: eng
text: Quantifying behaviors of robots which were generated autonomously from task-independent
objective functions is an important prerequisite for objective comparisons of
algorithms and movements of animals. The temporal sequence of such a behavior
can be considered as a time series and hence complexity measures developed for
time series are natural candidates for its quantification. The predictive information
and the excess entropy are such complexity measures. They measure the amount of
information the past contains about the future and thus quantify the nonrandom
structure in the temporal sequence. However, when using these measures for systems
with continuous states one has to deal with the fact that their values will depend
on the resolution with which the systems states are observed. For deterministic
systems both measures will diverge with increasing resolution. We therefore propose
a new decomposition of the excess entropy in resolution dependent and resolution
independent parts and discuss how they depend on the dimensionality of the dynamics,
correlations and the noise level. For the practical estimation we propose to use
estimates based on the correlation integral instead of the direct estimation of
the mutual information based on next neighbor statistics because the latter allows
less control of the scale dependencies. Using our algorithm we are able to show
how autonomous learning generates behavior of increasing complexity with increasing
learning duration.
acknowledgement: This work was supported by the DFG priority program 1527 (Autonomous
Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013)
under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie
Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013)
under REA grant agreement no. 291734.
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Eckehard
full_name: Olbrich, Eckehard
last_name: Olbrich
citation:
ama: Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots.
Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266
apa: Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of
autonomous robots. Entropy. MDPI. https://doi.org/10.3390/e17107266
chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior
of Autonomous Robots.” Entropy. MDPI, 2015. https://doi.org/10.3390/e17107266.
ieee: G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous
robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
ista: Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots.
Entropy. 17(10), 7266–7297.
mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of
Autonomous Robots.” Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97,
doi:10.3390/e17107266.
short: G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.
date_created: 2018-12-11T11:53:17Z
date_published: 2015-10-23T00:00:00Z
date_updated: 2023-10-17T11:42:00Z
day: '23'
ddc:
- '000'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3390/e17107266
ec_funded: 1
file:
- access_level: open_access
checksum: 945d99631a96e0315acb26dc8541dcf9
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:25Z
date_updated: 2020-07-14T12:45:08Z
file_id: '4943'
file_name: IST-2016-464-v1+1_entropy-17-07266.pdf
file_size: 6455007
relation: main_file
file_date_updated: 2020-07-14T12:45:08Z
has_accepted_license: '1'
intvolume: ' 17'
issue: '10'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 7266 - 7297
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Entropy
publication_status: published
publisher: MDPI
publist_id: '5495'
pubrep_id: '464'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying emergent behavior of autonomous robots
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2015'
...
---
_id: '1829'
abstract:
- lang: eng
text: Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or
baseball batting, depend on predictions where the ball can be intercepted and
how it can properly be returned to the opponent. These predictions get more accurate
over time, hence the behaviors need to be continuously modified. As a result,
movement templates with a learned global shape need to be adapted during the execution
so that the racket reaches a target position and velocity that will return the
ball over to the other side of the net or court. It requires altering learned
movements to hit a varying target with the necessary velocity at a specific instant
in time. Such a task cannot be incorporated straightforwardly in most movement
representations suitable for learning. For example, the standard formulation of
the dynamical system based motor primitives (introduced by Ijspeert et al (2002b))
does not satisfy this property despite their flexibility which has allowed learning
tasks ranging from locomotion to kendama. In order to fulfill this requirement,
we reformulate the Ijspeert framework to incorporate the possibility of specifying
a desired hitting point and a desired hitting velocity while maintaining all advantages
of the original formulation.We show that the proposed movement template formulation
works well in two scenarios, i.e., for hitting a ball on a string with a table
tennis racket at a specified velocity and for returning balls launched by a ball
gun successfully over the net using forehand movements.
alternative_title:
- Springer Tracts in Advanced Robotics
author:
- first_name: Katharina
full_name: Muelling, Katharina
last_name: Muelling
- first_name: Oliver
full_name: Kroemer, Oliver
last_name: Kroemer
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Muelling K, Kroemer O, Lampert C, Schölkopf B. Movement templates for learning
of hitting and batting. In: Kober J, Peters J, eds. Learning Motor Skills.
Vol 97. From Algorithms to Robot Experiments. Springer; 2014:69-82. doi:10.1007/978-3-319-03194-1_3'
apa: Muelling, K., Kroemer, O., Lampert, C., & Schölkopf, B. (2014). Movement
templates for learning of hitting and batting. In J. Kober & J. Peters (Eds.),
Learning Motor Skills (Vol. 97, pp. 69–82). Springer. https://doi.org/10.1007/978-3-319-03194-1_3
chicago: Muelling, Katharina, Oliver Kroemer, Christoph Lampert, and Bernhard Schölkopf.
“Movement Templates for Learning of Hitting and Batting.” In Learning Motor
Skills, edited by Jens Kober and Jan Peters, 97:69–82. From Algorithms to
Robot Experiments. Springer, 2014. https://doi.org/10.1007/978-3-319-03194-1_3.
ieee: K. Muelling, O. Kroemer, C. Lampert, and B. Schölkopf, “Movement templates
for learning of hitting and batting,” in Learning Motor Skills, vol. 97,
J. Kober and J. Peters, Eds. Springer, 2014, pp. 69–82.
ista: 'Muelling K, Kroemer O, Lampert C, Schölkopf B. 2014.Movement templates for
learning of hitting and batting. In: Learning Motor Skills. Springer Tracts in
Advanced Robotics, vol. 97, 69–82.'
mla: Muelling, Katharina, et al. “Movement Templates for Learning of Hitting and
Batting.” Learning Motor Skills, edited by Jens Kober and Jan Peters, vol.
97, Springer, 2014, pp. 69–82, doi:10.1007/978-3-319-03194-1_3.
short: K. Muelling, O. Kroemer, C. Lampert, B. Schölkopf, in:, J. Kober, J. Peters
(Eds.), Learning Motor Skills, Springer, 2014, pp. 69–82.
date_created: 2018-12-11T11:54:14Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2021-01-12T06:53:28Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-03194-1_3
editor:
- first_name: Jens
full_name: Kober, Jens
last_name: Kober
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
intvolume: ' 97'
language:
- iso: eng
month: '01'
oa_version: None
page: 69 - 82
publication: Learning Motor Skills
publication_status: published
publisher: Springer
publist_id: '5274'
quality_controlled: '1'
scopus_import: 1
series_title: From Algorithms to Robot Experiments
status: public
title: Movement templates for learning of hitting and batting
type: book_chapter
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2014'
...
---
_id: '2033'
abstract:
- lang: eng
text: 'The learning with privileged information setting has recently attracted a
lot of attention within the machine learning community, as it allows the integration
of additional knowledge into the training process of a classifier, even when this
comes in the form of a data modality that is not available at test time. Here,
we show that privileged information can naturally be treated as noise in the latent
function of a Gaussian process classifier (GPC). That is, in contrast to the standard
GPC setting, the latent function is not just a nuisance but a feature: it becomes
a natural measure of confidence about the training data by modulating the slope
of the GPC probit likelihood function. Extensive experiments on public datasets
show that the proposed GPC method using privileged noise, called GPC+, improves
over a standard GPC without privileged knowledge, and also over the current state-of-the-art
SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep
learning methods can be compressed as privileged information.'
author:
- first_name: Daniel
full_name: Hernandez Lobato, Daniel
last_name: Hernandez Lobato
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Kristian
full_name: Kersting, Kristian
last_name: Kersting
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
citation:
ama: 'Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. Mind
the nuisance: Gaussian process classification using privileged noise. In: Advances
in Neural Information Processing Systems. Vol 1. Neural Information Processing
Systems; 2014:837-845.'
apa: 'Hernandez Lobato, D., Sharmanska, V., Kersting, K., Lampert, C., & Quadrianto,
N. (2014). Mind the nuisance: Gaussian process classification using privileged
noise. In Advances in Neural Information Processing Systems (Vol. 1, pp.
837–845). Montreal, Canada: Neural Information Processing Systems.'
chicago: 'Hernandez Lobato, Daniel, Viktoriia Sharmanska, Kristian Kersting, Christoph
Lampert, and Novi Quadrianto. “Mind the Nuisance: Gaussian Process Classification
Using Privileged Noise.” In Advances in Neural Information Processing Systems,
1:837–45. Neural Information Processing Systems, 2014.'
ieee: 'D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, and N. Quadrianto,
“Mind the nuisance: Gaussian process classification using privileged noise,” in
Advances in Neural Information Processing Systems, Montreal, Canada, 2014,
vol. 1, no. January, pp. 837–845.'
ista: 'Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. 2014.
Mind the nuisance: Gaussian process classification using privileged noise. Advances
in Neural Information Processing Systems. NIPS: Neural Information Processing
Systems vol. 1, 837–845.'
mla: 'Hernandez Lobato, Daniel, et al. “Mind the Nuisance: Gaussian Process Classification
Using Privileged Noise.” Advances in Neural Information Processing Systems,
vol. 1, no. January, Neural Information Processing Systems, 2014, pp. 837–45.'
short: D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, N. Quadrianto,
in:, Advances in Neural Information Processing Systems, Neural Information Processing
Systems, 2014, pp. 837–845.
conference:
end_date: 2014-12-13
location: Montreal, Canada
name: 'NIPS: Neural Information Processing Systems'
start_date: 2014-12-08
date_created: 2018-12-11T11:55:20Z
date_published: 2014-12-08T00:00:00Z
date_updated: 2023-02-23T10:25:24Z
day: '08'
department:
- _id: ChLa
intvolume: ' 1'
issue: January
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.nips.cc/paper/5373-mind-the-nuisance-gaussian-process-classification-using-privileged-noise
month: '12'
oa: 1
oa_version: Submitted Version
page: 837-845
publication: Advances in Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5038'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Mind the nuisance: Gaussian process classification using privileged noise'
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2014'
...
---
_id: '2057'
abstract:
- lang: eng
text: 'In the past few years, a lot of attention has been devoted to multimedia
indexing by fusing multimodal informations. Two kinds of fusion schemes are generally
considered: The early fusion and the late fusion. We focus on late classifier
fusion, where one combines the scores of each modality at the decision level.
To tackle this problem, we investigate a recent and elegant well-founded quadratic
program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq
looks for the weighted combination, over a set of real-valued functions seen as
voters, leading to the lowest misclassification rate, while maximizing the voters’
diversity. We propose an extension of MinCq tailored to multimedia indexing. Our
method is based on an order-preserving pairwise loss adapted to ranking that allows
us to improve Mean Averaged Precision measure while taking into account the diversity
of the voters that we want to fuse. We provide evidence that this method is naturally
adapted to late fusion procedures and confirm the good behavior of our approach
on the challenging PASCAL VOC’07 benchmark.'
alternative_title:
- LNCS
author:
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
- first_name: Amaury
full_name: Habrard, Amaury
last_name: Habrard
- first_name: Stéphane
full_name: Ayache, Stéphane
last_name: Ayache
citation:
ama: 'Morvant E, Habrard A, Ayache S. Majority vote of diverse classifiers for late
fusion. In: Lecture Notes in Computer Science (Including Subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol
8621. Springer; 2014:153-162. doi:10.1007/978-3-662-44415-3_16'
apa: 'Morvant, E., Habrard, A., & Ayache, S. (2014). Majority vote of diverse
classifiers for late fusion. In Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 8621, pp. 153–162). Joensuu, Finland: Springer. https://doi.org/10.1007/978-3-662-44415-3_16'
chicago: Morvant, Emilie, Amaury Habrard, and Stéphane Ayache. “Majority Vote of
Diverse Classifiers for Late Fusion.” In Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics), 8621:153–62. Springer, 2014. https://doi.org/10.1007/978-3-662-44415-3_16.
ieee: E. Morvant, A. Habrard, and S. Ayache, “Majority vote of diverse classifiers
for late fusion,” in Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
Joensuu, Finland, 2014, vol. 8621, pp. 153–162.
ista: 'Morvant E, Habrard A, Ayache S. 2014. Majority vote of diverse classifiers
for late fusion. Lecture Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). IAPR: International
Workshop on Structural, Syntactic, and Statistical Pattern Recognition, LNCS,
vol. 8621, 153–162.'
mla: Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.”
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), vol. 8621, Springer, 2014,
pp. 153–62, doi:10.1007/978-3-662-44415-3_16.
short: E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics), Springer, 2014, pp. 153–162.
conference:
end_date: 2014-08-22
location: Joensuu, Finland
name: 'IAPR: International Workshop on Structural, Syntactic, and Statistical Pattern
Recognition'
start_date: 2014-08-20
date_created: 2018-12-11T11:55:28Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2021-01-12T06:55:01Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-662-44415-3_16
ec_funded: 1
external_id:
arxiv:
- '1404.7796'
intvolume: ' 8621'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1404.7796
month: '01'
oa: 1
oa_version: Preprint
page: 153 - 162
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_status: published
publisher: Springer
publist_id: '4989'
quality_controlled: '1'
scopus_import: 1
status: public
title: Majority vote of diverse classifiers for late fusion
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8621
year: '2014'
...
---
_id: '2171'
abstract:
- lang: eng
text: We present LS-CRF, a new method for training cyclic Conditional Random Fields
(CRFs) from large datasets that is inspired by classical closed-form expressions
for the maximum likelihood parameters of a generative graphical model with tree
topology. Training a CRF with LS-CRF requires only solving a set of independent
regression problems, each of which can be solved efficiently in closed form or
by an iterative solver. This makes LS-CRF orders of magnitude faster than classical
CRF training based on probabilistic inference, and at the same time more flexible
and easier to implement than other approximate techniques, such as pseudolikelihood
or piecewise training. We apply LS-CRF to the task of semantic image segmentation,
showing that it achieves on par accuracy to other training techniques at higher
speed, thereby allowing efficient CRF training from very large training sets.
For example, training a linearly parameterized pairwise CRF on 150,000 images
requires less than one hour on a modern workstation.
alternative_title:
- LNCS
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Matthieu
full_name: Guillaumin, Matthieu
last_name: Guillaumin
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. Closed-form approximate
CRF training for scalable image segmentation. In: Fleet D, Pajdla T, Schiele B,
Tuytelaars T, eds. Lecture Notes in Computer Science (Including Subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol
8691. Springer; 2014:550-565. doi:10.1007/978-3-319-10578-9_36'
apa: 'Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form
approximate CRF training for scalable image segmentation. In D. Fleet, T. Pajdla,
B. Schiele, & T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36'
chicago: Kolesnikov, Alexander, Matthieu Guillaumin, Vittorio Ferrari, and Christoph
Lampert. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David
Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 8691:550–65. Springer,
2014. https://doi.org/10.1007/978-3-319-10578-9_36.
ieee: A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. Lampert, “Closed-form approximate
CRF training for scalable image segmentation,” in Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), Zurich, Switzerland, 2014, vol. 8691, no. PART 3,
pp. 550–565.
ista: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. 2014. Closed-form approximate
CRF training for scalable image segmentation. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics). ECCV: European Conference on Computer Vision, LNCS, vol. 8691,
550–565.'
mla: Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable
Image Segmentation.” Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65,
doi:10.1007/978-3-319-10578-9_36.
short: A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla,
B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including
Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
Springer, 2014, pp. 550–565.
conference:
end_date: 2014-09-12
location: Zurich, Switzerland
name: 'ECCV: European Conference on Computer Vision'
start_date: 2014-09-06
date_created: 2018-12-11T11:56:07Z
date_published: 2014-09-01T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-10578-9_36
ec_funded: 1
editor:
- first_name: David
full_name: Fleet, David
last_name: Fleet
- first_name: Tomas
full_name: Pajdla, Tomas
last_name: Pajdla
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Tinne
full_name: Tuytelaars, Tinne
last_name: Tuytelaars
intvolume: ' 8691'
issue: PART 3
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1403.7057
month: '09'
oa: 1
oa_version: Submitted Version
page: 550 - 565
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_status: published
publisher: Springer
publist_id: '4813'
quality_controlled: '1'
scopus_import: 1
status: public
title: Closed-form approximate CRF training for scalable image segmentation
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 8691
year: '2014'
...
---
_id: '2173'
abstract:
- lang: eng
text: "In this work we introduce a new approach to co-classification, i.e. the task
of jointly classifying multiple, otherwise independent, data samples. The method
we present, named CoConut, is based on the idea of adding a regularizer in the
label space to encode certain priors on the resulting labelings. A regularizer
that encourages labelings that are smooth across the test set, for instance, can
be seen as a test-time variant of the cluster assumption, which has been proven
useful at training time in semi-supervised learning. A regularizer that introduces
a preference for certain class proportions can be regarded as a prior distribution
on the class labels. CoConut can build on existing classifiers without making
any assumptions on how they were obtained and without the need to re-train them.
The use of a regularizer adds a new level of flexibility. It allows the integration
of potentially new information at test time, even in other modalities than what
the classifiers were trained on. We evaluate our framework on six datasets, reporting
a clear performance gain in classification accuracy compared to the standard classification
setup that predicts labels for each test sample separately.\r\n"
author:
- first_name: Sameh
full_name: Khamis, Sameh
last_name: Khamis
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Khamis S, Lampert C. CoConut: Co-classification with output space regularization.
In: Proceedings of the British Machine Vision Conference 2014. BMVA Press;
2014.'
apa: 'Khamis, S., & Lampert, C. (2014). CoConut: Co-classification with output
space regularization. In Proceedings of the British Machine Vision Conference
2014. Nottingham, UK: BMVA Press.'
chicago: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with
Output Space Regularization.” In Proceedings of the British Machine Vision
Conference 2014. BMVA Press, 2014.'
ieee: 'S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,”
in Proceedings of the British Machine Vision Conference 2014, Nottingham,
UK, 2014.'
ista: 'Khamis S, Lampert C. 2014. CoConut: Co-classification with output space regularization.
Proceedings of the British Machine Vision Conference 2014. BMVC: British Machine
Vision Conference.'
mla: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output
Space Regularization.” Proceedings of the British Machine Vision Conference
2014, BMVA Press, 2014.'
short: S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference
2014, BMVA Press, 2014.
conference:
end_date: 2014-09-05
location: Nottingham, UK
name: 'BMVC: British Machine Vision Conference'
start_date: 2014-09-01
date_created: 2018-12-11T11:56:08Z
date_published: 2014-09-01T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
ec_funded: 1
file:
- access_level: open_access
checksum: c4c6d3efdb8ee648faf3e76849839ce2
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:08:23Z
date_updated: 2020-07-14T12:45:31Z
file_id: '4683'
file_name: IST-2016-490-v1+1_khamis-bmvc2014.pdf
file_size: 408172
relation: main_file
file_date_updated: 2020-07-14T12:45:31Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2014
publication_status: published
publisher: BMVA Press
publist_id: '4811'
pubrep_id: '490'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'CoConut: Co-classification with output space regularization'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2014'
...
---
_id: '2172'
abstract:
- lang: eng
text: Fisher Kernels and Deep Learning were two developments with significant impact
on large-scale object categorization in the last years. Both approaches were shown
to achieve state-of-the-art results on large-scale object categorization datasets,
such as ImageNet. Conceptually, however, they are perceived as very different
and it is not uncommon for heated debates to spring up when advocates of both
paradigms meet at conferences or workshops. In this work, we emphasize the similarities
between both architectures rather than their differences and we argue that such
a unified view allows us to transfer ideas from one domain to the other. As a
concrete example we introduce a method for learning a support vector machine classifier
with Fisher kernel at the same time as a task-specific data representation. We
reinterpret the setting as a multi-layer feed forward network. Its final layer
is the classifier, parameterized by a weight vector, and the two previous layers
compute Fisher vectors, parameterized by the coefficients of a Gaussian mixture
model. We introduce a gradient descent based learning algorithm that, in contrast
to other feature learning techniques, is not just derived from intuition or biological
analogy, but has a theoretical justification in the framework of statistical learning
theory. Our experiments show that the new training procedure leads to significant
improvements in classification accuracy while preserving the modularity and geometric
interpretability of a support vector machine setup.
author:
- first_name: Vladyslav
full_name: Sydorov, Vladyslav
last_name: Sydorov
- first_name: Mayu
full_name: Sakurada, Mayu
last_name: Sakurada
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sydorov V, Sakurada M, Lampert C. Deep Fisher Kernels – End to end learning
of the Fisher Kernel GMM parameters. In: Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition. IEEE; 2014:1402-1409.
doi:10.1109/CVPR.2014.182'
apa: 'Sydorov, V., Sakurada, M., & Lampert, C. (2014). Deep Fisher Kernels –
End to end learning of the Fisher Kernel GMM parameters. In Proceedings of
the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(pp. 1402–1409). Columbus, USA: IEEE. https://doi.org/10.1109/CVPR.2014.182'
chicago: Sydorov, Vladyslav, Mayu Sakurada, and Christoph Lampert. “Deep Fisher
Kernels – End to End Learning of the Fisher Kernel GMM Parameters.” In Proceedings
of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,
1402–9. IEEE, 2014. https://doi.org/10.1109/CVPR.2014.182.
ieee: V. Sydorov, M. Sakurada, and C. Lampert, “Deep Fisher Kernels – End to end
learning of the Fisher Kernel GMM parameters,” in Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, Columbus, USA,
2014, pp. 1402–1409.
ista: 'Sydorov V, Sakurada M, Lampert C. 2014. Deep Fisher Kernels – End to end
learning of the Fisher Kernel GMM parameters. Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition. CVPR: Computer
Vision and Pattern Recognition, 1402–1409.'
mla: Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the
Fisher Kernel GMM Parameters.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182.
short: V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp.
1402–1409.
conference:
end_date: 2014-06-28
location: Columbus, USA
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2014-06-23
date_created: 2018-12-11T11:56:08Z
date_published: 2014-09-24T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '24'
department:
- _id: ChLa
doi: 10.1109/CVPR.2014.182
ec_funded: 1
language:
- iso: eng
month: '09'
oa_version: None
page: 1402 - 1409
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_status: published
publisher: IEEE
publist_id: '4812'
quality_controlled: '1'
scopus_import: 1
status: public
title: Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2014'
...
---
_id: '2180'
abstract:
- lang: eng
text: Weighted majority votes allow one to combine the output of several classifiers
or voters. MinCq is a recent algorithm for optimizing the weight of each voter
based on the minimization of a theoretical bound over the risk of the vote with
elegant PAC-Bayesian generalization guarantees. However, while it has demonstrated
good performance when combining weak classifiers, MinCq cannot make use of the
useful a priori knowledge that one may have when using a mixture of weak and strong
voters. In this paper, we propose P-MinCq, an extension of MinCq that can incorporate
such knowledge in the form of a constraint over the distribution of the weights,
along with general proofs of convergence that stand in the sample compression
setting for data-dependent voters. The approach is applied to a vote of k-NN classifiers
with a specific modeling of the voters' performance. P-MinCq significantly outperforms
the classic k-NN classifier, a symmetric NN and MinCq using the same voters. We
show that it is also competitive with LMNN, a popular metric learning algorithm,
and that combining both approaches further reduces the error.
acknowledgement: 'This work was funded by the French project SoLSTiCe ANR-13-BS02-01
of the ANR. '
author:
- first_name: Aurélien
full_name: Bellet, Aurélien
last_name: Bellet
- first_name: Amaury
full_name: Habrard, Amaury
last_name: Habrard
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
- first_name: Marc
full_name: Sebban, Marc
last_name: Sebban
citation:
ama: Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted
majority votes. Machine Learning. 2014;97(1-2):129-154. doi:10.1007/s10994-014-5462-z
apa: Bellet, A., Habrard, A., Morvant, E., & Sebban, M. (2014). Learning a priori
constrained weighted majority votes. Machine Learning. Springer. https://doi.org/10.1007/s10994-014-5462-z
chicago: Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning
a Priori Constrained Weighted Majority Votes.” Machine Learning. Springer,
2014. https://doi.org/10.1007/s10994-014-5462-z.
ieee: A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained
weighted majority votes,” Machine Learning, vol. 97, no. 1–2. Springer,
pp. 129–154, 2014.
ista: Bellet A, Habrard A, Morvant E, Sebban M. 2014. Learning a priori constrained
weighted majority votes. Machine Learning. 97(1–2), 129–154.
mla: Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.”
Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z.
short: A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014)
129–154.
date_created: 2018-12-11T11:56:10Z
date_published: 2014-10-01T00:00:00Z
date_updated: 2021-01-12T06:55:49Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/s10994-014-5462-z
ec_funded: 1
intvolume: ' 97'
issue: 1-2
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hal.archives-ouvertes.fr/hal-01009578/document
month: '10'
oa: 1
oa_version: Submitted Version
page: 129 - 154
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Machine Learning
publication_status: published
publisher: Springer
publist_id: '4802'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning a priori constrained weighted majority votes
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2014'
...
---
_id: '2189'
abstract:
- lang: fre
text: En apprentissage automatique, nous parlons d'adaptation de domaine lorsque
les données de test (cibles) et d'apprentissage (sources) sont générées selon
différentes distributions. Nous devons donc développer des algorithmes de classification
capables de s'adapter à une nouvelle distribution, pour laquelle aucune information
sur les étiquettes n'est disponible. Nous attaquons cette problématique sous l'angle
de l'approche PAC-Bayésienne qui se focalise sur l'apprentissage de modèles définis
comme des votes de majorité sur un ensemble de fonctions. Dans ce contexte, nous
introduisons PV-MinCq une version adaptative de l'algorithme (non adaptatif) MinCq.
PV-MinCq suit le principe suivant. Nous transférons les étiquettes sources aux
points cibles proches pour ensuite appliquer MinCq sur l'échantillon cible ``auto-étiqueté''
(justifié par une borne théorique). Plus précisément, nous définissons un auto-étiquetage
non itératif qui se focalise dans les régions où les distributions marginales
source et cible sont les plus similaires. Dans un second temps, nous étudions
l'influence de notre auto-étiquetage pour en déduire une procédure de validation
des hyperparamètres. Finalement, notre approche montre des résultats empiriques
prometteurs.
article_processing_charge: No
author:
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
citation:
ama: 'Morvant E. Adaptation de domaine de vote de majorité par auto-étiquetage non
itératif. In: Vol 1. Elsevier; 2014:49-58.'
apa: 'Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone
sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne,
France: Elsevier.'
chicago: Morvant, Emilie. “Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage
Non Itératif,” 1:49–58. Elsevier, 2014.
ieee: 'E. Morvant, “Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif,” presented at the CAP: Conférence Francophone sur l’Apprentissage
Automatique (Machine Learning French Conference), Saint-Etienne, France, 2014,
vol. 1, pp. 49–58.'
ista: 'Morvant E. 2014. Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif. CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine
Learning French Conference) vol. 1, 49–58.'
mla: Morvant, Emilie. Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage
Non Itératif. Vol. 1, Elsevier, 2014, pp. 49–58.
short: E. Morvant, in:, Elsevier, 2014, pp. 49–58.
conference:
location: Saint-Etienne, France
name: 'CAP: Conférence Francophone sur l''Apprentissage Automatique (Machine Learning
French Conference)'
date_created: 2018-12-11T11:56:13Z
date_published: 2014-07-01T00:00:00Z
date_updated: 2021-01-12T06:55:52Z
day: '01'
department:
- _id: ChLa
intvolume: ' 1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hal.archives-ouvertes.fr/hal-01005776/
month: '07'
oa: 1
oa_version: Preprint
page: 49-58
publication_status: published
publisher: Elsevier
publist_id: '4785'
quality_controlled: '1'
status: public
title: Adaptation de domaine de vote de majorité par auto-étiquetage non itératif
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2014'
...
---
_id: '2160'
abstract:
- lang: eng
text: Transfer learning has received a lot of attention in the machine learning
community over the last years, and several effective algorithms have been developed.
However, relatively little is known about their theoretical properties, especially
in the setting of lifelong learning, where the goal is to transfer information
to tasks for which no data have been observed so far. In this work we study lifelong
learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization
bound that offers a unified view on existing paradigms for transfer learning,
such as the transfer of parameters or the transfer of low-dimensional representations.
We also use the bound to derive two principled lifelong learning algorithms, and
we show that these yield results comparable with existing methods.
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol
32. ML Research Press; 2014:991-999.'
apa: 'Pentina, A., & Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning
(Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine
Learning, Beijing, China: ML Research Press.'
chicago: Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong
Learning,” 32:991–99. ML Research Press, 2014.
ieee: 'A. Pentina and C. Lampert, “A PAC-Bayesian bound for Lifelong Learning,”
presented at the ICML: International Conference on Machine Learning, Beijing,
China, 2014, vol. 32, pp. 991–999.'
ista: 'Pentina A, Lampert C. 2014. A PAC-Bayesian bound for Lifelong Learning. ICML:
International Conference on Machine Learning vol. 32, 991–999.'
mla: Pentina, Anastasia, and Christoph Lampert. A PAC-Bayesian Bound for Lifelong
Learning. Vol. 32, ML Research Press, 2014, pp. 991–99.
short: A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.
conference:
end_date: 2014-06-26
location: Beijing, China
name: 'ICML: International Conference on Machine Learning'
start_date: 2014-06-21
date_created: 2018-12-11T11:56:03Z
date_published: 2014-05-10T00:00:00Z
date_updated: 2023-10-17T11:54:24Z
day: '10'
department:
- _id: ChLa
intvolume: ' 32'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/citation.cfm?id=3045003
month: '05'
oa: 1
oa_version: Submitted Version
page: 991 - 999
publication_status: published
publisher: ML Research Press
publist_id: '4844'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A PAC-Bayesian bound for Lifelong Learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2014'
...
---
_id: '2294'
abstract:
- lang: eng
text: "In this work we propose a system for automatic classification of Drosophila
embryos into developmental stages.\r\nWhile the system is designed to solve an
actual problem in biological research, we believe that the principle underly-\r\ning
it is interesting not only for biologists, but also for researchers in computer
vision. The main idea is to combine two orthogonal sources of information: one
is a classifier trained on strongly invariant features, which makes it applicable
to images of very different conditions, but also leads to rather noisy predictions.
The other is a label propagation step based on a more powerful similarity measure
that however is only consistent within specific subsets of the data at a time.\r\nIn
our biological setup, the information sources are the shape and the staining patterns
of embryo images. We show\r\nexperimentally that while neither of the methods
\ can be used by itself to achieve satisfactory results, their combina-\r\ntion
achieves prediction quality comparable to human performance."
author:
- first_name: Tomas
full_name: Kazmar, Tomas
last_name: Kazmar
- first_name: Evgeny
full_name: Kvon, Evgeny
last_name: Kvon
- first_name: Alexander
full_name: Stark, Alexander
last_name: Stark
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kazmar T, Kvon E, Stark A, Lampert C. Drosophila Embryo Stage Annotation using
Label Propagation. In: IEEE; 2013. doi:10.1109/ICCV.2013.139'
apa: 'Kazmar, T., Kvon, E., Stark, A., & Lampert, C. (2013). Drosophila Embryo
Stage Annotation using Label Propagation. Presented at the ICCV: International
Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.139'
chicago: Kazmar, Tomas, Evgeny Kvon, Alexander Stark, and Christoph Lampert. “Drosophila
Embryo Stage Annotation Using Label Propagation.” IEEE, 2013. https://doi.org/10.1109/ICCV.2013.139.
ieee: 'T. Kazmar, E. Kvon, A. Stark, and C. Lampert, “Drosophila Embryo Stage Annotation
using Label Propagation,” presented at the ICCV: International Conference on Computer
Vision, Sydney, Australia, 2013.'
ista: 'Kazmar T, Kvon E, Stark A, Lampert C. 2013. Drosophila Embryo Stage Annotation
using Label Propagation. ICCV: International Conference on Computer Vision.'
mla: Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation.
IEEE, 2013, doi:10.1109/ICCV.2013.139.
short: T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013.
conference:
end_date: 2013-12-08
location: Sydney, Australia
name: 'ICCV: International Conference on Computer Vision'
start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2021-01-12T06:56:35Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.139
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.cv-foundation.org/openaccess/ICCV2013.py
month: '12'
oa: 1
oa_version: Submitted Version
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4634'
quality_controlled: '1'
scopus_import: 1
status: public
title: Drosophila Embryo Stage Annotation using Label Propagation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2293'
abstract:
- lang: eng
text: Many computer vision problems have an asymmetric distribution of information
between training and test time. In this work, we study the case where we are given
additional information about the training data, which however will not be available
at test time. This situation is called learning using privileged information (LUPI).
We introduce two maximum-margin techniques that are able to make use of this additional
source of information, and we show that the framework is applicable to several
scenarios that have been studied in computer vision before. Experiments with attributes,
bounding boxes, image tags and rationales as additional information in object
classification show promising results.
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information.
In: IEEE; 2013:825-832. doi:10.1109/ICCV.2013.107'
apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2013). Learning to rank
using privileged information (pp. 825–832). Presented at the ICCV: International
Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.107'
chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning
to Rank Using Privileged Information,” 825–32. IEEE, 2013. https://doi.org/10.1109/ICCV.2013.107.
ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Learning to rank using privileged
information,” presented at the ICCV: International Conference on Computer Vision,
Sydney, Australia, 2013, pp. 825–832.'
ista: 'Sharmanska V, Quadrianto N, Lampert C. 2013. Learning to rank using privileged
information. ICCV: International Conference on Computer Vision, 825–832.'
mla: Sharmanska, Viktoriia, et al. Learning to Rank Using Privileged Information.
IEEE, 2013, pp. 825–32, doi:10.1109/ICCV.2013.107.
short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.
conference:
end_date: 2013-12-08
location: Sydney, Australia
name: 'ICCV: International Conference on Computer Vision'
start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2023-02-23T10:36:41Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.107
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: www.cv-foundation.org/openaccess/content_iccv_2013/papers/Sharmanska_Learning_to_Rank_2013_ICCV_paper.pdf
month: '12'
oa: 1
oa_version: Submitted Version
page: 825 - 832
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4635'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning to rank using privileged information
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2516'
abstract:
- lang: eng
text: 'We study the problem of object recognition for categories for which we have
no training examples, a task also called zero-data or zero-shot learning. This
situation has hardly been studied in computer vision research, even though it
occurs frequently: the world contains tens of thousands of different object classes
and for only few of them image collections have been formed and suitably annotated.
To tackle the problem we introduce attribute-based classification: objects are
identified based on a high-level description that is phrased in terms of semantic
attributes, such as the object''s color or shape. Because the identification of
each such property transcends the specific learning task at hand, the attribute
classifiers can be pre-learned independently, e.g. from existing image datasets
unrelated to the current task. Afterwards, new classes can be detected based on
their attribute representation, without the need for a new training phase. In
this paper we also introduce a new dataset, Animals with Attributes, of over 30,000
images of 50 animal classes, annotated with 85 semantic attributes. Extensive
experiments on this and two more datasets show that attribute-based classification
indeed is able to categorize images without access to any training images of the
target classes.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Hannes
full_name: Nickisch, Hannes
last_name: Nickisch
- first_name: Stefan
full_name: Harmeling, Stefan
last_name: Harmeling
citation:
ama: Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot
learning of object categories. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140
apa: Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification
for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2013.140
chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based
Classification for Zero-Shot Learning of Object Categories.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2013. https://doi.org/10.1109/TPAMI.2013.140.
ieee: C. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification
for zero-shot learning of object categories,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 36, no. 3. IEEE, pp. 453–465, 2013.
ista: Lampert C, Nickisch H, Harmeling S. 2013. Attribute-based classification for
zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 36(3), 453–465.
mla: Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning
of Object Categories.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140.
short: C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis
and Machine Intelligence 36 (2013) 453–465.
date_created: 2018-12-11T11:58:08Z
date_published: 2013-07-30T00:00:00Z
date_updated: 2021-01-12T06:57:58Z
day: '30'
department:
- _id: ChLa
doi: 10.1109/TPAMI.2013.140
intvolume: ' 36'
issue: '3'
language:
- iso: eng
month: '07'
oa_version: None
page: 453 - 465
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '4385'
quality_controlled: '1'
scopus_import: 1
status: public
title: Attribute-based classification for zero-shot learning of object categories
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2013'
...
---
_id: '2520'
abstract:
- lang: eng
text: "We propose a probabilistic model to infer supervised latent variables in\r\nthe
Hamming space from observed data. Our model allows simultaneous\r\ninference of
the number of binary latent variables, and their values. The\r\nlatent variables
preserve neighbourhood structure of the data in a sense\r\nthat objects in the
same semantic concept have similar latent values, and\r\nobjects in different
concepts have dissimilar latent values. We formulate\r\nthe supervised infinite
latent variable problem based on an intuitive\r\nprinciple of pulling objects
together if they are of the same type, and\r\npushing them apart if they are not.
We then combine this principle with a\r\nflexible Indian Buffet Process prior
on the latent variables. We show that\r\nthe inferred supervised latent variables
can be directly used to perform a\r\nnearest neighbour search for the purpose
of retrieval. We introduce a new\r\napplication of dynamically extending hash
codes, and show how to\r\neffectively couple the structure of the hash codes with
continuously\r\ngrowing structure of the neighbourhood preserving infinite latent
feature\r\nspace."
author:
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: David
full_name: Knowles, David
last_name: Knowles
- first_name: Zoubin
full_name: Ghahramani, Zoubin
last_name: Ghahramani
citation:
ama: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood
preserving infinite latent feature models. In: Proceedings of the 29th Conference
Uncertainty in Artificial Intelligence. AUAI Press; 2013:527-536.'
apa: 'Quadrianto, N., Sharmanska, V., Knowles, D., & Ghahramani, Z. (2013).
The supervised IBP: Neighbourhood preserving infinite latent feature models. In
Proceedings of the 29th conference uncertainty in Artificial Intelligence
(pp. 527–536). Bellevue, WA, United States: AUAI Press.'
chicago: 'Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani.
“The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.”
In Proceedings of the 29th Conference Uncertainty in Artificial Intelligence,
527–36. AUAI Press, 2013.'
ieee: 'N. Quadrianto, V. Sharmanska, D. Knowles, and Z. Ghahramani, “The supervised
IBP: Neighbourhood preserving infinite latent feature models,” in Proceedings
of the 29th conference uncertainty in Artificial Intelligence, Bellevue, WA,
United States, 2013, pp. 527–536.'
ista: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. 2013. The supervised
IBP: Neighbourhood preserving infinite latent feature models. Proceedings of the
29th conference uncertainty in Artificial Intelligence. UAI: Uncertainty in Artificial
Intelligence, 527–536.'
mla: 'Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite
Latent Feature Models.” Proceedings of the 29th Conference Uncertainty in Artificial
Intelligence, AUAI Press, 2013, pp. 527–36.'
short: N. Quadrianto, V. Sharmanska, D. Knowles, Z. Ghahramani, in:, Proceedings
of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013,
pp. 527–536.
conference:
end_date: 2013-07-15
location: Bellevue, WA, United States
name: 'UAI: Uncertainty in Artificial Intelligence'
start_date: 2013-07-11
date_created: 2018-12-11T11:58:09Z
date_published: 2013-07-11T00:00:00Z
date_updated: 2023-02-23T10:46:36Z
day: '11'
ddc:
- '000'
department:
- _id: ChLa
file:
- access_level: open_access
checksum: 325f20c4b926bd74d39006b97df572bd
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:16Z
date_updated: 2020-07-14T12:45:42Z
file_id: '5134'
file_name: IST-2013-137-v1+1_QuaShaKnoGha13.pdf
file_size: 1117100
relation: main_file
file_date_updated: 2020-07-14T12:45:42Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 527 - 536
publication: Proceedings of the 29th conference uncertainty in Artificial Intelligence
publication_identifier:
isbn:
- '9780974903996'
publication_status: published
publisher: AUAI Press
publist_id: '4381'
pubrep_id: '137'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'The supervised IBP: Neighbourhood preserving infinite latent feature models'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2901'
abstract:
- lang: eng
text: ' We introduce the M-modes problem for graphical models: predicting the M
label configurations of highest probability that are at the same time local maxima
of the probability landscape. M-modes have multiple possible applications: because
they are intrinsically diverse, they provide a principled alternative to non-maximum
suppression techniques for structured prediction, they can act as codebook vectors
for quantizing the configuration space, or they can form component centers for
mixture model approximation. We present two algorithms for solving the M-modes
problem. The first algorithm solves the problem in polynomial time when the underlying
graphical model is a simple chain. The second algorithm solves the problem for
junction chains. In synthetic and real dataset, we demonstrate how M-modes can
improve the performance of prediction. We also use the generated modes as a tool
to understand the topography of the probability distribution of configurations,
for example with relation to the training set size and amount of noise in the
data. '
alternative_title:
- ' JMLR: W&CP'
author:
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Zhu
full_name: Yan, Zhu
last_name: Yan
- first_name: Dimitris
full_name: Metaxas, Dimitris
last_name: Metaxas
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. Computing the M most probable
modes of a graphical model. In: Vol 31. JMLR; 2013:161-169.'
apa: 'Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., & Lampert, C. (2013).
Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169).
Presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence,
Scottsdale, AZ, United States: JMLR.'
chicago: Chen, Chao, Vladimir Kolmogorov, Zhu Yan, Dimitris Metaxas, and Christoph
Lampert. “Computing the M Most Probable Modes of a Graphical Model,” 31:161–69.
JMLR, 2013.
ieee: 'C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the
M most probable modes of a graphical model,” presented at the AISTATS: Conference
on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013,
vol. 31, pp. 161–169.'
ista: 'Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. 2013. Computing the M
most probable modes of a graphical model. AISTATS: Conference on Uncertainty
in Artificial Intelligence, JMLR: W&CP, vol. 31, 161–169.'
mla: Chen, Chao, et al. Computing the M Most Probable Modes of a Graphical Model.
Vol. 31, JMLR, 2013, pp. 161–69.
short: C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013,
pp. 161–169.
conference:
end_date: 2013-05-01
location: Scottsdale, AZ, United States
name: ' AISTATS: Conference on Uncertainty in Artificial Intelligence'
start_date: 2013-04-29
date_created: 2018-12-11T12:00:14Z
date_published: 2013-01-01T00:00:00Z
date_updated: 2021-01-12T07:00:35Z
day: '01'
department:
- _id: HeEd
- _id: VlKo
- _id: ChLa
intvolume: ' 31'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://jmlr.org/proceedings/papers/v31/chen13a.html
month: '01'
oa: 1
oa_version: None
page: 161 - 169
publication_status: published
publisher: JMLR
publist_id: '3846'
quality_controlled: '1'
scopus_import: 1
status: public
title: Computing the M most probable modes of a graphical model
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2013'
...
---
_id: '2948'
abstract:
- lang: eng
text: 'Many visual datasets are traditionally used to analyze the performance of
different learning techniques. The evaluation is usually done within each dataset,
therefore it is questionable if such results are a reliable indicator of true
generalization ability. We propose here an algorithm to exploit the existing data
resources when learning on a new multiclass problem. Our main idea is to identify
an image representation that decomposes orthogonally into two subspaces: a part
specific to each dataset, and a part generic to, and therefore shared between,
all the considered source sets. This allows us to use the generic representation
as un-biased reference knowledge for a novel classification task. By casting the
method in the multi-view setting, we also make it possible to use different features
for different databases. We call the algorithm MUST, Multitask Unaligned Shared
knowledge Transfer. Through extensive experiments on five public datasets, we
show that MUST consistently improves the cross-datasets generalization performance.'
acknowledgement: This work was supported by the PASCAL 2 Network of Excellence (TT)
and by the Newton International Fellowship (NQ)
alternative_title:
- LNCS
author:
- first_name: Tatiana
full_name: Tommasi, Tatiana
last_name: Tommasi
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Barbara
full_name: Caputo, Barbara
last_name: Caputo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Tommasi T, Quadrianto N, Caputo B, Lampert C. Beyond dataset bias: Multi-task
unaligned shared knowledge transfer. 2013;7724:1-15. doi:10.1007/978-3-642-37331-2_1'
apa: 'Tommasi, T., Quadrianto, N., Caputo, B., & Lampert, C. (2013). Beyond
dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the
ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer. https://doi.org/10.1007/978-3-642-37331-2_1'
chicago: 'Tommasi, Tatiana, Novi Quadrianto, Barbara Caputo, and Christoph Lampert.
“Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer.” Lecture
Notes in Computer Science. Springer, 2013. https://doi.org/10.1007/978-3-642-37331-2_1.'
ieee: 'T. Tommasi, N. Quadrianto, B. Caputo, and C. Lampert, “Beyond dataset bias:
Multi-task unaligned shared knowledge transfer,” vol. 7724. Springer, pp. 1–15,
2013.'
ista: 'Tommasi T, Quadrianto N, Caputo B, Lampert C. 2013. Beyond dataset bias:
Multi-task unaligned shared knowledge transfer. 7724, 1–15.'
mla: 'Tommasi, Tatiana, et al. Beyond Dataset Bias: Multi-Task Unaligned Shared
Knowledge Transfer. Vol. 7724, Springer, 2013, pp. 1–15, doi:10.1007/978-3-642-37331-2_1.'
short: T. Tommasi, N. Quadrianto, B. Caputo, C. Lampert, 7724 (2013) 1–15.
conference:
end_date: 2012-11-09
location: Daejeon, Korea
name: 'ACCV: Asian Conference on Computer Vision'
start_date: 2012-11-05
date_created: 2018-12-11T12:00:30Z
date_published: 2013-04-04T00:00:00Z
date_updated: 2020-08-11T10:09:54Z
day: '04'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-37331-2_1
file:
- access_level: open_access
checksum: a0a7234a89e2192af655b0d0ae3bf445
content_type: application/pdf
creator: dernst
date_created: 2019-01-22T14:03:11Z
date_updated: 2020-07-14T12:45:55Z
file_id: '5874'
file_name: 2012_ACCV_Tommasi.pdf
file_size: 1513620
relation: main_file
file_date_updated: 2020-07-14T12:45:55Z
has_accepted_license: '1'
intvolume: ' 7724'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
page: 1 - 15
publication_status: published
publisher: Springer
publist_id: '3784'
quality_controlled: '1'
scopus_import: 1
series_title: Lecture Notes in Computer Science
status: public
title: 'Beyond dataset bias: Multi-task unaligned shared knowledge transfer'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7724
year: '2013'
...
---
_id: '3321'
author:
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Quadrianto N, Lampert C. Kernel based learning. In: Dubitzky W, Wolkenhauer
O, Cho K, Yokota H, eds. Encyclopedia of Systems Biology. Vol 3. Springer;
2013:1069-1069. doi:10.1007/978-1-4419-9863-7_604'
apa: Quadrianto, N., & Lampert, C. (2013). Kernel based learning. In W. Dubitzky,
O. Wolkenhauer, K. Cho, & H. Yokota (Eds.), Encyclopedia of Systems Biology
(Vol. 3, pp. 1069–1069). Springer. https://doi.org/10.1007/978-1-4419-9863-7_604
chicago: Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.” In Encyclopedia
of Systems Biology, edited by Werner Dubitzky, Olaf Wolkenhauer, Kwang Cho,
and Hiroki Yokota, 3:1069–1069. Springer, 2013. https://doi.org/10.1007/978-1-4419-9863-7_604.
ieee: N. Quadrianto and C. Lampert, “Kernel based learning,” in Encyclopedia
of Systems Biology, vol. 3, W. Dubitzky, O. Wolkenhauer, K. Cho, and H. Yokota,
Eds. Springer, 2013, pp. 1069–1069.
ista: 'Quadrianto N, Lampert C. 2013.Kernel based learning. In: Encyclopedia of
Systems Biology. vol. 3, 1069–1069.'
mla: Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.” Encyclopedia
of Systems Biology, edited by Werner Dubitzky et al., vol. 3, Springer, 2013,
pp. 1069–1069, doi:10.1007/978-1-4419-9863-7_604.
short: N. Quadrianto, C. Lampert, in:, W. Dubitzky, O. Wolkenhauer, K. Cho, H. Yokota
(Eds.), Encyclopedia of Systems Biology, Springer, 2013, pp. 1069–1069.
date_created: 2018-12-11T12:02:39Z
date_published: 2013-01-01T00:00:00Z
date_updated: 2021-01-12T07:42:38Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-1-4419-9863-7_604
editor:
- first_name: Werner
full_name: Dubitzky, Werner
last_name: Dubitzky
- first_name: Olaf
full_name: Wolkenhauer, Olaf
last_name: Wolkenhauer
- first_name: Kwang
full_name: Cho, Kwang
last_name: Cho
- first_name: Hiroki
full_name: Yokota, Hiroki
last_name: Yokota
intvolume: ' 3'
language:
- iso: eng
month: '01'
oa_version: None
page: 1069 - 1069
publication: Encyclopedia of Systems Biology
publication_status: published
publisher: Springer
publist_id: '3314'
quality_controlled: '1'
status: public
title: Kernel based learning
type: encyclopedia_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2013'
...
---
_id: '2825'
abstract:
- lang: eng
text: 'We study the problem of maximum marginal prediction (MMP) in probabilistic
graphical models, a task that occurs, for example, as the Bayes optimal decision
rule under a Hamming loss. MMP is typically performed as a two-stage procedure:
one estimates each variable''s marginal probability and then forms a prediction
from the states of maximal probability. In this work we propose a simple yet effective
technique for accelerating MMP when inference is sampling-based: instead of the
above two-stage procedure we directly estimate the posterior probability of each
decision variable. This allows us to identify the point of time when we are sufficiently
certain about any individual decision. Whenever this is the case, we dynamically
prune the variables we are confident about from the underlying factor graph. Consequently,
at any time only samples of variables whose decision is still uncertain need to
be created. Experiments in two prototypical scenarios, multi-label classification
and image inpainting, show that adaptive sampling can drastically accelerate MMP
without sacrificing prediction accuracy.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Dynamic pruning of factor graphs for maximum marginal prediction.
In: Vol 1. Neural Information Processing Systems; 2012:82-90.'
apa: 'Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal
prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing
Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.'
chicago: Lampert, Christoph. “Dynamic Pruning of Factor Graphs for Maximum Marginal
Prediction,” 1:82–90. Neural Information Processing Systems, 2012.
ieee: 'C. Lampert, “Dynamic pruning of factor graphs for maximum marginal prediction,”
presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV,
United States, 2012, vol. 1, pp. 82–90.'
ista: 'Lampert C. 2012. Dynamic pruning of factor graphs for maximum marginal prediction.
NIPS: Neural Information Processing Systems vol. 1, 82–90.'
mla: Lampert, Christoph. Dynamic Pruning of Factor Graphs for Maximum Marginal
Prediction. Vol. 1, Neural Information Processing Systems, 2012, pp. 82–90.
short: C. Lampert, in:, Neural Information Processing Systems, 2012, pp. 82–90.
conference:
end_date: 2012-12-06
location: Lake Tahoe, NV, United States
name: 'NIPS: Neural Information Processing Systems'
start_date: 2012-12-03
date_created: 2018-12-11T11:59:48Z
date_published: 2012-12-01T00:00:00Z
date_updated: 2021-01-12T06:59:59Z
day: '01'
department:
- _id: ChLa
intvolume: ' 1'
language:
- iso: eng
month: '12'
oa_version: None
page: 82 - 90
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '3975'
quality_controlled: '1'
scopus_import: 1
status: public
title: Dynamic pruning of factor graphs for maximum marginal prediction
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2012'
...
---
_id: '3164'
abstract:
- lang: eng
text: Overview of the Special Issue on structured prediction and inference.
author:
- first_name: Matthew
full_name: Blaschko, Matthew
last_name: Blaschko
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Blaschko M, Lampert C. Guest editorial: Special issue on structured prediction
and inference. International Journal of Computer Vision. 2012;99(3):257-258.
doi:10.1007/s11263-012-0530-y'
apa: 'Blaschko, M., & Lampert, C. (2012). Guest editorial: Special issue on
structured prediction and inference. International Journal of Computer Vision.
Springer. https://doi.org/10.1007/s11263-012-0530-y'
chicago: 'Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue
on Structured Prediction and Inference.” International Journal of Computer
Vision. Springer, 2012. https://doi.org/10.1007/s11263-012-0530-y.'
ieee: 'M. Blaschko and C. Lampert, “Guest editorial: Special issue on structured
prediction and inference,” International Journal of Computer Vision, vol.
99, no. 3. Springer, pp. 257–258, 2012.'
ista: 'Blaschko M, Lampert C. 2012. Guest editorial: Special issue on structured
prediction and inference. International Journal of Computer Vision. 99(3), 257–258.'
mla: 'Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue
on Structured Prediction and Inference.” International Journal of Computer
Vision, vol. 99, no. 3, Springer, 2012, pp. 257–58, doi:10.1007/s11263-012-0530-y.'
short: M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012)
257–258.
date_created: 2018-12-11T12:01:46Z
date_published: 2012-09-01T00:00:00Z
date_updated: 2021-01-12T07:41:30Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/s11263-012-0530-y
intvolume: ' 99'
issue: '3'
language:
- iso: eng
month: '09'
oa_version: None
page: 257 - 258
publication: International Journal of Computer Vision
publication_status: published
publisher: Springer
publist_id: '3521'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Guest editorial: Special issue on structured prediction and inference'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 99
year: '2012'
...
---
_id: '3125'
abstract:
- lang: eng
text: We propose a new learning method to infer a mid-level feature representation
that combines the advantage of semantic attribute representations with the higher
expressive power of non-semantic features. The idea lies in augmenting an existing
attribute-based representation with additional dimensions for which an autoencoder
model is coupled with a large-margin principle. This construction allows a smooth
transition between the zero-shot regime with no training example, the unsupervised
regime with training examples but without class labels, and the supervised regime
with training examples and with class labels. The resulting optimization problem
can be solved efficiently, because several of the necessity steps have closed-form
solutions. Through extensive experiments we show that the augmented representation
achieves better results in terms of object categorization accuracy than the semantic
representation alone.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sharmanska V, Quadrianto N, Lampert C. Augmented attribute representations.
In: Vol 7576. Springer; 2012:242-255. doi:10.1007/978-3-642-33715-4_18'
apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2012). Augmented attribute
representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference
on Computer Vision, Florence, Italy: Springer. https://doi.org/10.1007/978-3-642-33715-4_18'
chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Augmented
Attribute Representations,” 7576:242–55. Springer, 2012. https://doi.org/10.1007/978-3-642-33715-4_18.
ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Augmented attribute representations,”
presented at the ECCV: European Conference on Computer Vision, Florence, Italy,
2012, vol. 7576, no. PART 5, pp. 242–255.'
ista: 'Sharmanska V, Quadrianto N, Lampert C. 2012. Augmented attribute representations.
ECCV: European Conference on Computer Vision, LNCS, vol. 7576, 242–255.'
mla: Sharmanska, Viktoriia, et al. Augmented Attribute Representations. Vol.
7576, no. PART 5, Springer, 2012, pp. 242–55, doi:10.1007/978-3-642-33715-4_18.
short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255.
conference:
end_date: 2012-10-13
location: Florence, Italy
name: 'ECCV: European Conference on Computer Vision'
start_date: 2012-10-07
date_created: 2018-12-11T12:01:32Z
date_published: 2012-10-01T00:00:00Z
date_updated: 2023-02-23T11:13:25Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-33715-4_18
file:
- access_level: open_access
checksum: bccdbe0663780d25a1e0524002b2d896
content_type: application/pdf
creator: dernst
date_created: 2020-05-15T12:29:04Z
date_updated: 2020-07-14T12:46:00Z
file_id: '7861'
file_name: 2012_ECCV_Sharmanska.pdf
file_size: 6073897
relation: main_file
file_date_updated: 2020-07-14T12:46:00Z
has_accepted_license: '1'
intvolume: ' 7576'
issue: PART 5
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 242 - 255
publication_status: published
publisher: Springer
publist_id: '3574'
quality_controlled: '1'
scopus_import: 1
status: public
title: Augmented attribute representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7576
year: '2012'
...
---
_id: '3126'
abstract:
- lang: eng
text: "In this work we propose a new information-theoretic clustering algorithm
that infers cluster memberships by direct optimization of a non-parametric mutual
information estimate between data distribution and cluster assignment. Although
the optimization objective has a solid theoretical foundation it is hard to optimize.
We propose an approximate optimization formulation that leads to an efficient
algorithm with low runtime complexity. The algorithm has a single free parameter,
the number of clusters to find. We demonstrate superior performance on several
synthetic and real datasets.\r\n"
alternative_title:
- LNCS
author:
- first_name: Andreas
full_name: Müller, Andreas
last_name: Müller
- first_name: Sebastian
full_name: Nowozin, Sebastian
last_name: Nowozin
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Müller A, Nowozin S, Lampert C. Information theoretic clustering using minimal
spanning trees. In: Vol 7476. Springer; 2012:205-215. doi:10.1007/978-3-642-32717-9_21'
apa: 'Müller, A., Nowozin, S., & Lampert, C. (2012). Information theoretic clustering
using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM:
German Association For Pattern Recognition, Graz, Austria: Springer. https://doi.org/10.1007/978-3-642-32717-9_21'
chicago: Müller, Andreas, Sebastian Nowozin, and Christoph Lampert. “Information
Theoretic Clustering Using Minimal Spanning Trees,” 7476:205–15. Springer, 2012.
https://doi.org/10.1007/978-3-642-32717-9_21.
ieee: 'A. Müller, S. Nowozin, and C. Lampert, “Information theoretic clustering
using minimal spanning trees,” presented at the DAGM: German Association For Pattern
Recognition, Graz, Austria, 2012, vol. 7476, pp. 205–215.'
ista: 'Müller A, Nowozin S, Lampert C. 2012. Information theoretic clustering using
minimal spanning trees. DAGM: German Association For Pattern Recognition, LNCS,
vol. 7476, 205–215.'
mla: Müller, Andreas, et al. Information Theoretic Clustering Using Minimal Spanning
Trees. Vol. 7476, Springer, 2012, pp. 205–15, doi:10.1007/978-3-642-32717-9_21.
short: A. Müller, S. Nowozin, C. Lampert, in:, Springer, 2012, pp. 205–215.
conference:
end_date: 2012-08-31
location: Graz, Austria
name: 'DAGM: German Association For Pattern Recognition'
start_date: 2012-08-28
date_created: 2018-12-11T12:01:32Z
date_published: 2012-08-14T00:00:00Z
date_updated: 2021-01-12T07:41:14Z
day: '14'
department:
- _id: ChLa
doi: 10.1007/978-3-642-32717-9_21
intvolume: ' 7476'
language:
- iso: eng
month: '08'
oa_version: None
page: 205 - 215
publication_status: published
publisher: Springer
publist_id: '3573'
quality_controlled: '1'
scopus_import: 1
status: public
title: Information theoretic clustering using minimal spanning trees
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 7476
year: '2012'
...
---
_id: '3248'
abstract:
- lang: eng
text: We describe RTblob, a high speed vision system that detects objects in cluttered
scenes based on their color and shape at a speed of over 800 frames/s. Because
the system is available as open-source software and relies only on off-the-shelf
PC hardware components, it can provide the basis for multiple application scenarios.
As an illustrative example, we show how RTblob can be used in a robotic table
tennis scenario to estimate ball trajectories through 3D space simultaneously
from four cameras images at a speed of 200 Hz.
article_processing_charge: No
article_type: original
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: Lampert C, Peters J. Real-time detection of colored objects in multiple camera
streams with off-the-shelf hardware components. Journal of Real-Time Image
Processing. 2012;7(1):31-41. doi:10.1007/s11554-010-0168-3
apa: Lampert, C., & Peters, J. (2012). Real-time detection of colored objects
in multiple camera streams with off-the-shelf hardware components. Journal
of Real-Time Image Processing. Springer. https://doi.org/10.1007/s11554-010-0168-3
chicago: Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects
in Multiple Camera Streams with off-the-Shelf Hardware Components.” Journal
of Real-Time Image Processing. Springer, 2012. https://doi.org/10.1007/s11554-010-0168-3.
ieee: C. Lampert and J. Peters, “Real-time detection of colored objects in multiple
camera streams with off-the-shelf hardware components,” Journal of Real-Time
Image Processing, vol. 7, no. 1. Springer, pp. 31–41, 2012.
ista: Lampert C, Peters J. 2012. Real-time detection of colored objects in multiple
camera streams with off-the-shelf hardware components. Journal of Real-Time Image
Processing. 7(1), 31–41.
mla: Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects
in Multiple Camera Streams with off-the-Shelf Hardware Components.” Journal
of Real-Time Image Processing, vol. 7, no. 1, Springer, 2012, pp. 31–41, doi:10.1007/s11554-010-0168-3.
short: C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41.
date_created: 2018-12-11T12:02:15Z
date_published: 2012-03-01T00:00:00Z
date_updated: 2022-05-24T08:05:40Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/s11554-010-0168-3
file:
- access_level: open_access
checksum: 241be47ea50e81a283bcf4c45b07e8cc
content_type: application/pdf
creator: kschuh
date_created: 2019-02-12T10:52:25Z
date_updated: 2020-07-14T12:46:04Z
file_id: '5958'
file_name: 2012_Springer_Lampert.pdf
file_size: 2933187
relation: main_file
file_date_updated: 2020-07-14T12:46:04Z
has_accepted_license: '1'
intvolume: ' 7'
issue: '1'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Submitted Version
page: 31 - 41
publication: Journal of Real-Time Image Processing
publication_identifier:
eissn:
- 1861-8219
issn:
- 1861-8200
publication_status: published
publisher: Springer
publist_id: '3417'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Real-time detection of colored objects in multiple camera streams with off-the-shelf
hardware components
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7
year: '2012'
...
---
_id: '3124'
abstract:
- lang: eng
text: "We consider the problem of inference in a graphical model with binary variables.
While in theory it is arguably preferable to compute marginal probabilities, in
practice researchers often use MAP inference due to the availability of efficient
discrete optimization algorithms. We bridge the gap between the two approaches
by introducing the Discrete Marginals technique in which approximate marginals
are obtained by minimizing an objective function with unary and pairwise terms
over a discretized domain. This allows the use of techniques originally developed
for MAP-MRF inference and learning. We explore two ways to set up the objective
function - by discretizing the Bethe free energy and by learning it from training
data. Experimental results show that for certain types of graphs a learned function
can outperform the Bethe approximation. We also establish a link between the Bethe
free energy and submodular functions.\r\n"
alternative_title:
- Inferning 2012
author:
- first_name: Filip
full_name: Korc, Filip
id: 476A2FD6-F248-11E8-B48F-1D18A9856A87
last_name: Korc
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Korc F, Kolmogorov V, Lampert C. Approximating marginals using discrete energy
minimization. In: ICML; 2012.'
apa: 'Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals
using discrete energy minimization. Presented at the ICML: International Conference
on Machine Learning, Edinburgh, Scotland: ICML.'
chicago: Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. “Approximating
Marginals Using Discrete Energy Minimization.” ICML, 2012.
ieee: 'F. Korc, V. Kolmogorov, and C. Lampert, “Approximating marginals using discrete
energy minimization,” presented at the ICML: International Conference on Machine
Learning, Edinburgh, Scotland, 2012.'
ista: 'Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete
energy minimization. ICML: International Conference on Machine Learning, Inferning
2012, .'
mla: Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization.
ICML, 2012.
short: F. Korc, V. Kolmogorov, C. Lampert, in:, ICML, 2012.
conference:
end_date: 2012-07-01
location: Edinburgh, Scotland
name: 'ICML: International Conference on Machine Learning'
start_date: 2012-06-26
date_created: 2018-12-11T12:01:31Z
date_published: 2012-06-30T00:00:00Z
date_updated: 2023-02-23T12:24:24Z
day: '30'
ddc:
- '000'
department:
- _id: ChLa
- _id: VlKo
file:
- access_level: open_access
checksum: 3d0d4246548c736857302aadb2ff5d15
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:11:34Z
date_updated: 2020-07-14T12:46:00Z
file_id: '4889'
file_name: IST-2016-565-v1+1_DM-inferning2012.pdf
file_size: 305836
relation: main_file
file_date_updated: 2020-07-14T12:46:00Z
has_accepted_license: '1'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Submitted Version
publication_status: published
publisher: ICML
publist_id: '3575'
pubrep_id: '565'
quality_controlled: '1'
related_material:
record:
- id: '5396'
relation: later_version
status: public
status: public
title: Approximating marginals using discrete energy minimization
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2012'
...