---
_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'
...
---
_id: '5396'
abstract:
- lang: eng
text: We consider the problem of inference in agraphical 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 pair-wise terms
over a discretized domain. This allows the use of techniques originally devel-oped
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 out-perform the Bethe approximation. We also establish a link between the
Bethe free energy and submodular functions.
alternative_title:
- IST Austria Technical Report
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. IST Austria; 2012. doi:10.15479/AT:IST-2012-0003
apa: Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals
using discrete energy minimization. IST Austria. https://doi.org/10.15479/AT:IST-2012-0003
chicago: Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. Approximating
Marginals Using Discrete Energy Minimization. IST Austria, 2012. https://doi.org/10.15479/AT:IST-2012-0003.
ieee: F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete
energy minimization. IST Austria, 2012.
ista: Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete
energy minimization, IST Austria, 13p.
mla: Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization.
IST Austria, 2012, doi:10.15479/AT:IST-2012-0003.
short: F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete
Energy Minimization, IST Austria, 2012.
date_created: 2018-12-12T11:39:06Z
date_published: 2012-07-23T00:00:00Z
date_updated: 2023-02-23T11:13:22Z
day: '23'
ddc:
- '000'
department:
- _id: VlKo
- _id: ChLa
doi: 10.15479/AT:IST-2012-0003
file:
- access_level: open_access
checksum: 7e0ba85ad123b13223aaf6cdde2d288c
content_type: application/pdf
creator: system
date_created: 2018-12-12T11:53:29Z
date_updated: 2020-07-14T12:46:44Z
file_id: '5490'
file_name: IST-2012-0003_IST-2012-0003.pdf
file_size: 618744
relation: main_file
file_date_updated: 2020-07-14T12:46:44Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '13'
publication_identifier:
issn:
- 2664-1690
publication_status: published
publisher: IST Austria
pubrep_id: '36'
related_material:
record:
- id: '3124'
relation: earlier_version
status: public
status: public
title: Approximating marginals using discrete energy minimization
type: technical_report
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '2915'
acknowledgement: "The project receives funding from the European Community’s Seventh
Framework Programme under grant agreement\r\nno. ICT- 248273 GeRT."
article_processing_charge: No
author:
- 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: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: 'Kroemer O, Lampert C, Peters J. Multi-modal learning for dynamic tactile sensing.
In: Deutsches Zentrum für Luft und Raumfahrt; 2012.'
apa: Kroemer, O., Lampert, C., & Peters, J. (2012). Multi-modal learning for
dynamic tactile sensing. Deutsches Zentrum für Luft und Raumfahrt.
chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Multi-Modal Learning
for Dynamic Tactile Sensing.” Deutsches Zentrum für Luft und Raumfahrt, 2012.
ieee: O. Kroemer, C. Lampert, and J. Peters, “Multi-modal learning for dynamic tactile
sensing,” 2012.
ista: Kroemer O, Lampert C, Peters J. 2012. Multi-modal learning for dynamic tactile
sensing
mla: Kroemer, Oliver, et al. Multi-Modal Learning for Dynamic Tactile Sensing.
Deutsches Zentrum für Luft und Raumfahrt, 2012.
short: O. Kroemer, C. Lampert, J. Peters, in:, Deutsches Zentrum für Luft und Raumfahrt,
2012.
date_created: 2018-12-11T12:00:19Z
date_published: 2012-10-11T00:00:00Z
date_updated: 2023-10-17T07:58:59Z
day: '11'
department:
- _id: ChLa
language:
- iso: eng
month: '10'
oa_version: None
publication_status: published
publisher: Deutsches Zentrum für Luft und Raumfahrt
publist_id: '3828'
quality_controlled: '1'
status: public
title: Multi-modal learning for dynamic tactile sensing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '3127'
abstract:
- lang: eng
text: "When searching for characteristic subpatterns in potentially noisy graph
data, it appears self-evident that having multiple observations would be better
than having just one. However, it turns out that the inconsistencies introduced
when different graph instances have different edge sets pose a serious challenge.
In this work we address this challenge for the problem of finding maximum weighted
cliques.\r\n We introduce the concept of most persistent soft-clique. This
is subset of vertices, that 1) is almost fully or at least densely connected,
2) occurs in all or almost all graph instances, and 3) has the maximum weight.
We present a measure of clique-ness, that essentially counts the number of edge
missing to make a subset of vertices into a clique. With this measure, we show
that the problem of finding the most persistent soft-clique problem can be cast
either as: a) a max-min two person game optimization problem, or b) a min-min
soft margin optimization problem. Both formulations lead to the same solution
when using a partial Lagrangian method to solve the optimization problems. By
experiments on synthetic data and on real social network data, we show that the
proposed method is able to reliably find soft cliques in graph data, even if that
is distorted by random noise or unreliable observations."
article_processing_charge: No
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
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
citation:
ama: 'Quadrianto N, Lampert C, Chen C. The most persistent soft-clique in a set
of sampled graphs. In: Proceedings of the 29th International Conference on
Machine Learning. ML Research Press; 2012:211-218.'
apa: 'Quadrianto, N., Lampert, C., & Chen, C. (2012). The most persistent soft-clique
in a set of sampled graphs. In Proceedings of the 29th International Conference
on Machine Learning (pp. 211–218). Edinburgh, United Kingdom: ML Research
Press.'
chicago: Quadrianto, Novi, Christoph Lampert, and Chao Chen. “The Most Persistent
Soft-Clique in a Set of Sampled Graphs.” In Proceedings of the 29th International
Conference on Machine Learning, 211–18. ML Research Press, 2012.
ieee: N. Quadrianto, C. Lampert, and C. Chen, “The most persistent soft-clique in
a set of sampled graphs,” in Proceedings of the 29th International Conference
on Machine Learning, Edinburgh, United Kingdom, 2012, pp. 211–218.
ista: 'Quadrianto N, Lampert C, Chen C. 2012. The most persistent soft-clique in
a set of sampled graphs. Proceedings of the 29th International Conference on Machine
Learning. ICML: International Conference on Machine Learning, 211–218.'
mla: Quadrianto, Novi, et al. “The Most Persistent Soft-Clique in a Set of Sampled
Graphs.” Proceedings of the 29th International Conference on Machine Learning,
ML Research Press, 2012, pp. 211–18.
short: N. Quadrianto, C. Lampert, C. Chen, in:, Proceedings of the 29th International
Conference on Machine Learning, ML Research Press, 2012, pp. 211–218.
conference:
end_date: 2012-07-01
location: Edinburgh, United Kingdom
name: 'ICML: International Conference on Machine Learning'
start_date: 2012-06-26
date_created: 2018-12-11T12:01:33Z
date_published: 2012-06-01T00:00:00Z
date_updated: 2023-10-17T11:55:06Z
day: '01'
department:
- _id: ChLa
- _id: HeEd
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1206.4652
month: '06'
oa: 1
oa_version: Preprint
page: 211-218
publication: Proceedings of the 29th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
publist_id: '3572'
quality_controlled: '1'
scopus_import: '1'
status: public
title: The most persistent soft-clique in a set of sampled graphs
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '3337'
abstract:
- lang: eng
text: Playing table tennis is a difficult task for robots, especially due to their
limitations of acceleration. A key bottleneck is the amount of time needed to
reach the desired hitting position and velocity of the racket for returning the
incoming ball. Here, it often does not suffice to simply extrapolate the ball's
trajectory after the opponent returns it but more information is needed. Humans
are able to predict the ball's trajectory based on the opponent's moves and, thus,
have a considerable advantage. Hence, we propose to incorporate an anticipation
system into robot table tennis players, which enables the robot to react earlier
while the opponent is performing the striking movement. Based on visual observation
of the opponent's racket movement, the robot can predict the aim of the opponent
and adjust its movement generation accordingly. The policies for deciding how
and when to react are obtained by reinforcement learning. We conduct experiments
with an existing robot player to show that the learned reaction policy can significantly
improve the performance of the overall system.
author:
- first_name: Zhikun
full_name: Wang, Zhikun
last_name: Wang
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Katharina
full_name: Mülling, Katharina
last_name: Mülling
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: 'Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. Learning anticipation
policies for robot table tennis. In: IEEE; 2011:332-337. doi:10.1109/IROS.2011.6094892'
apa: 'Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., & Peters, J. (2011).
Learning anticipation policies for robot table tennis (pp. 332–337). Presented
at the IROS: RSJ International Conference on Intelligent Robots and Systems, San
Francisco, USA: IEEE. https://doi.org/10.1109/IROS.2011.6094892'
chicago: Wang, Zhikun, Christoph Lampert, Katharina Mülling, Bernhard Schölkopf,
and Jan Peters. “Learning Anticipation Policies for Robot Table Tennis,” 332–37.
IEEE, 2011. https://doi.org/10.1109/IROS.2011.6094892.
ieee: 'Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, and J. Peters, “Learning anticipation
policies for robot table tennis,” presented at the IROS: RSJ International Conference
on Intelligent Robots and Systems, San Francisco, USA, 2011, pp. 332–337.'
ista: 'Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. 2011. Learning anticipation
policies for robot table tennis. IROS: RSJ International Conference on Intelligent
Robots and Systems, 332–337.'
mla: Wang, Zhikun, et al. Learning Anticipation Policies for Robot Table Tennis.
IEEE, 2011, pp. 332–37, doi:10.1109/IROS.2011.6094892.
short: Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011,
pp. 332–337.
conference:
end_date: 2011-09-30
location: San Francisco, USA
name: 'IROS: RSJ International Conference on Intelligent Robots and Systems'
start_date: 2011-09-25
date_created: 2018-12-11T12:02:45Z
date_published: 2011-01-01T00:00:00Z
date_updated: 2021-01-12T07:42:45Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/IROS.2011.6094892
language:
- iso: eng
month: '01'
oa_version: None
page: 332 - 337
publication_status: published
publisher: IEEE
publist_id: '3293'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning anticipation policies for robot table tennis
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3389'
abstract:
- lang: eng
text: Kernel canonical correlation analysis (KCCA) is a general technique for subspace
learning that incorporates principal components analysis (PCA) and Fisher linear
discriminant analysis (LDA) as special cases. By finding directions that maximize
correlation, KCCA learns representations that are more closely tied to the underlying
process that generates the data and can ignore high-variance noise directions.
However, for data where acquisition in one or more modalities is expensive or
otherwise limited, KCCA may suffer from small sample effects. We propose to use
semi-supervised Laplacian regularization to utilize data that are present in only
one modality. This approach is able to find highly correlated directions that
also lie along the data manifold, resulting in a more robust estimate of correlated
subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally
amenable to subspace techniques as data are well aligned. fMRI data of the human
brain are a particularly interesting candidate. In this study we implemented various
supervised and semi-supervised versions of KCCA on human fMRI data, with regression
to single and multi-variate labels (corresponding to video content subjects viewed
during the image acquisition). In each variate condition, the semi-supervised
variants of KCCA performed better than the supervised variants, including a supervised
variant with Laplacian regularization. We additionally analyze the weights learned
by the regression in order to infer brain regions that are important to different
types of visual processing.
acknowledgement: The research leading to these results has received funding from the
European Research Council under the European Community’s Seventh Framework Programme
(FP7/2007-2013)/ERC Grant Agreement No. 228180. This work was funded in part by
the EC project CLASS, IST 027978, and the PASCAL2 network of excellence, IST 2002-506778.
author:
- first_name: Matthew
full_name: Blaschko, Matthew
last_name: Blaschko
- first_name: Jacquelyn
full_name: Shelton, Jacquelyn
last_name: Shelton
- first_name: Andreas
full_name: Bartels, Andreas
last_name: Bartels
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Arthur
full_name: Gretton, Arthur
last_name: Gretton
citation:
ama: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel
canonical correlation analysis with application to human fMRI. Pattern Recognition
Letters. 2011;32(11):1572-1583. doi:10.1016/j.patrec.2011.02.011
apa: Blaschko, M., Shelton, J., Bartels, A., Lampert, C., & Gretton, A. (2011).
Semi supervised kernel canonical correlation analysis with application to human
fMRI. Pattern Recognition Letters. Elsevier. https://doi.org/10.1016/j.patrec.2011.02.011
chicago: Blaschko, Matthew, Jacquelyn Shelton, Andreas Bartels, Christoph Lampert,
and Arthur Gretton. “Semi Supervised Kernel Canonical Correlation Analysis with
Application to Human FMRI.” Pattern Recognition Letters. Elsevier, 2011.
https://doi.org/10.1016/j.patrec.2011.02.011.
ieee: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, and A. Gretton, “Semi supervised
kernel canonical correlation analysis with application to human fMRI,” Pattern
Recognition Letters, vol. 32, no. 11. Elsevier, pp. 1572–1583, 2011.
ista: Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. 2011. Semi supervised
kernel canonical correlation analysis with application to human fMRI. Pattern
Recognition Letters. 32(11), 1572–1583.
mla: Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis
with Application to Human FMRI.” Pattern Recognition Letters, vol. 32,
no. 11, Elsevier, 2011, pp. 1572–83, doi:10.1016/j.patrec.2011.02.011.
short: M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition
Letters 32 (2011) 1572–1583.
date_created: 2018-12-11T12:03:03Z
date_published: 2011-08-01T00:00:00Z
date_updated: 2021-01-12T07:43:09Z
day: '01'
department:
- _id: ChLa
doi: 10.1016/j.patrec.2011.02.011
intvolume: ' 32'
issue: '11'
language:
- iso: eng
month: '08'
oa_version: None
page: 1572 - 1583
publication: Pattern Recognition Letters
publication_status: published
publisher: Elsevier
publist_id: '3218'
quality_controlled: '1'
scopus_import: 1
status: public
title: Semi supervised kernel canonical correlation analysis with application to human
fMRI
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2011'
...
---
_id: '3382'
abstract:
- lang: eng
text: Dynamic tactile sensing is a fundamental ability to recognize materials and
objects. However, while humans are born with partially developed dynamic tactile
sensing and quickly master this skill, today's robots remain in their infancy.
The development of such a sense requires not only better sensors but the right
algorithms to deal with these sensors' data as well. For example, when classifying
a material based on touch, the data are noisy, high-dimensional, and contain irrelevant
signals as well as essential ones. Few classification methods from machine learning
can deal with such problems. In this paper, we propose an efficient approach to
infer suitable lower dimensional representations of the tactile data. In order
to classify materials based on only the sense of touch, these representations
are autonomously discovered using visual information of the surfaces during training.
However, accurately pairing vision and tactile samples in real-robot applications
is a difficult problem. The proposed approach, therefore, works with weak pairings
between the modalities. Experiments show that the resulting approach is very robust
and yields significantly higher classification performance based on only dynamic
tactile sensing.
author:
- 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: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust
vision based training. IEEE Transactions on Robotics. 2011;27(3):545-557.
doi:10.1109/TRO.2011.2121130
apa: Kroemer, O., Lampert, C., & Peters, J. (2011). Learning dynamic tactile
sensing with robust vision based training. IEEE Transactions on Robotics.
IEEE. https://doi.org/10.1109/TRO.2011.2121130
chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Learning Dynamic Tactile
Sensing with Robust Vision Based Training.” IEEE Transactions on Robotics.
IEEE, 2011. https://doi.org/10.1109/TRO.2011.2121130.
ieee: O. Kroemer, C. Lampert, and J. Peters, “Learning dynamic tactile sensing with
robust vision based training,” IEEE Transactions on Robotics, vol. 27,
no. 3. IEEE, pp. 545–557, 2011.
ista: Kroemer O, Lampert C, Peters J. 2011. Learning dynamic tactile sensing with
robust vision based training. IEEE Transactions on Robotics. 27(3), 545–557.
mla: Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision
Based Training.” IEEE Transactions on Robotics, vol. 27, no. 3, IEEE, 2011,
pp. 545–57, doi:10.1109/TRO.2011.2121130.
short: O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011)
545–557.
date_created: 2018-12-11T12:03:01Z
date_published: 2011-05-21T00:00:00Z
date_updated: 2021-01-12T07:43:06Z
day: '21'
department:
- _id: ChLa
doi: 10.1109/TRO.2011.2121130
intvolume: ' 27'
issue: '3'
language:
- iso: eng
month: '05'
oa_version: None
page: 545 - 557
publication: IEEE Transactions on Robotics
publication_status: published
publisher: IEEE
publist_id: '3225'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning dynamic tactile sensing with robust vision based training
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2011'
...
---
_id: '5386'
abstract:
- lang: eng
text: 'We introduce TopoCut: a new way to integrate knowledge about topological
properties (TPs) into random field image segmentation model. Instead of including
TPs as additional constraints during minimization of the energy function, we devise
an efficient algorithm for modifying the unary potentials such that the resulting
segmentation is guaranteed with the desired properties. Our method is more flexible
in the sense that it handles more topology constraints than previous methods,
which were only able to enforce pairwise or global connectivity. In particular,
our method is very fast, making it for the first time possible to enforce global
topological properties in practical image segmentation tasks.'
alternative_title:
- IST Austria Technical Report
author:
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
- first_name: Daniel
full_name: Freedman, Daniel
last_name: Freedman
- 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, Freedman D, Lampert C. Enforcing Topological Constraints in Random
Field Image Segmentation. IST Austria; 2011. doi:10.15479/AT:IST-2011-0002
apa: Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological
constraints in random field image segmentation. IST Austria. https://doi.org/10.15479/AT:IST-2011-0002
chicago: Chen, Chao, Daniel Freedman, and Christoph Lampert. Enforcing Topological
Constraints in Random Field Image Segmentation. IST Austria, 2011. https://doi.org/10.15479/AT:IST-2011-0002.
ieee: C. Chen, D. Freedman, and C. Lampert, Enforcing topological constraints
in random field image segmentation. IST Austria, 2011.
ista: Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in
random field image segmentation, IST Austria, 69p.
mla: Chen, Chao, et al. Enforcing Topological Constraints in Random Field Image
Segmentation. IST Austria, 2011, doi:10.15479/AT:IST-2011-0002.
short: C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random
Field Image Segmentation, IST Austria, 2011.
date_created: 2018-12-12T11:39:02Z
date_published: 2011-03-28T00:00:00Z
date_updated: 2023-02-23T11:22:48Z
day: '28'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.15479/AT:IST-2011-0002
file:
- access_level: open_access
checksum: ad64c2add5fe2ad10e9d5c669f3f9526
content_type: application/pdf
creator: system
date_created: 2018-12-12T11:53:34Z
date_updated: 2020-07-14T12:46:41Z
file_id: '5495'
file_name: IST-2011-0002_IST-2011-0002.pdf
file_size: 26390601
relation: main_file
file_date_updated: 2020-07-14T12:46:41Z
has_accepted_license: '1'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '69'
publication_identifier:
issn:
- 2664-1690
publication_status: published
publisher: IST Austria
pubrep_id: '22'
related_material:
record:
- id: '3336'
relation: later_version
status: public
status: public
title: Enforcing topological constraints in random field image segmentation
type: technical_report
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3336'
abstract:
- lang: eng
text: 'We introduce TopoCut: a new way to integrate knowledge about topological
properties (TPs) into random field image segmentation model. Instead of including
TPs as additional constraints during minimization of the energy function, we devise
an efficient algorithm for modifying the unary potentials such that the resulting
segmentation is guaranteed with the desired properties. Our method is more flexible
in the sense that it handles more topology constraints than previous methods,
which were only able to enforce pairwise or global connectivity. In particular,
our method is very fast, making it for the first time possible to enforce global
topological properties in practical image segmentation tasks.'
acknowledgement: The first author is supported by the Austrian Science Fund (FWF)
grant No. P20134-N13. The authors would like to thank Sebastian Nowozin for helpful
discussions.
article_processing_charge: No
author:
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
- first_name: Daniel
full_name: Freedman, Daniel
last_name: Freedman
- 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, Freedman D, Lampert C. Enforcing topological constraints in random
field image segmentation. In: CVPR: Computer Vision and Pattern Recognition.
IEEE; 2011:2089-2096. doi:10.1109/CVPR.2011.5995503'
apa: 'Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints
in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition
(pp. 2089–2096). Colorado Springs, CO, United States: IEEE. https://doi.org/10.1109/CVPR.2011.5995503'
chicago: 'Chen, Chao, Daniel Freedman, and Christoph Lampert. “Enforcing Topological
Constraints in Random Field Image Segmentation.” In CVPR: Computer Vision and
Pattern Recognition, 2089–96. IEEE, 2011. https://doi.org/10.1109/CVPR.2011.5995503.'
ieee: 'C. Chen, D. Freedman, and C. Lampert, “Enforcing topological constraints
in random field image segmentation,” in CVPR: Computer Vision and Pattern Recognition,
Colorado Springs, CO, United States, 2011, pp. 2089–2096.'
ista: 'Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in
random field image segmentation. CVPR: Computer Vision and Pattern Recognition.
CVPR: Conference on Computer Vision and Pattern Recognition, 2089–2096.'
mla: 'Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image
Segmentation.” CVPR: Computer Vision and Pattern Recognition, IEEE, 2011,
pp. 2089–96, doi:10.1109/CVPR.2011.5995503.'
short: 'C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern
Recognition, IEEE, 2011, pp. 2089–2096.'
conference:
end_date: 2011-06-25
location: Colorado Springs, CO, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2011-06-20
date_created: 2018-12-11T12:02:45Z
date_published: 2011-07-22T00:00:00Z
date_updated: 2023-02-23T12:23:56Z
day: '22'
department:
- _id: HeEd
- _id: ChLa
doi: 10.1109/CVPR.2011.5995503
language:
- iso: eng
month: '07'
oa_version: None
page: 2089 - 2096
publication: 'CVPR: Computer Vision and Pattern Recognition'
publication_identifier:
eisbn:
- 978-1-4577-0395-9
isbn:
- 978-1-4577-0394-2
publication_status: published
publisher: IEEE
publist_id: '3294'
quality_controlled: '1'
related_material:
record:
- id: '5386'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Enforcing topological constraints in random field image segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3163'
abstract:
- lang: eng
text: We study multi-label prediction for structured output sets, a problem that
occurs, for example, in object detection in images, secondary structure prediction
in computational biology, and graph matching with symmetries. Conventional multilabel
classification techniques are typically not applicable in this situation, because
they require explicit enumeration of the label set, which is infeasible in case
of structured outputs. Relying on techniques originally designed for single-label
structured prediction, in particular structured support vector machines, results
in reduced prediction accuracy, or leads to infeasible optimization problems.
In this work we derive a maximum-margin training formulation for multi-label structured
prediction that remains computationally tractable while achieving high prediction
accuracy. It also shares most beneficial properties with single-label maximum-margin
approaches, in particular formulation as a convex optimization problem, efficient
working set training, and PAC-Bayesian generalization bounds.
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. Maximum margin multi-label structured prediction. In: Neural Information
Processing Systems; 2011.'
apa: 'Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented
at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information
Processing Systems.'
chicago: Lampert, Christoph. “Maximum Margin Multi-Label Structured Prediction.”
Neural Information Processing Systems, 2011.
ieee: 'C. Lampert, “Maximum margin multi-label structured prediction,” presented
at the NIPS: Neural Information Processing Systems, Granada, Spain, 2011.'
ista: 'Lampert C. 2011. Maximum margin multi-label structured prediction. NIPS:
Neural Information Processing Systems.'
mla: Lampert, Christoph. Maximum Margin Multi-Label Structured Prediction.
Neural Information Processing Systems, 2011.
short: C. Lampert, in:, Neural Information Processing Systems, 2011.
conference:
end_date: 2011-12-14
location: Granada, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2011-12-12
date_created: 2018-12-11T12:01:45Z
date_published: 2011-12-01T00:00:00Z
date_updated: 2023-10-17T11:47:35Z
day: '01'
department:
- _id: ChLa
language:
- iso: eng
month: '12'
oa_version: None
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '3522'
quality_controlled: '1'
related_material:
record:
- id: '3322'
relation: later_version
status: public
scopus_import: 1
status: public
title: Maximum margin multi-label structured prediction
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3322'
abstract:
- lang: eng
text: We study multi-label prediction for structured output spaces, a problem that
occurs, for example, in object detection in images, secondary structure prediction
in computational biology, and graph matching with symmetries. Conventional multi-label
classification techniques are typically not applicable in this situation, because
they require explicit enumeration of the label space, which is infeasible in case
of structured outputs. Relying on techniques originally designed for single- label
structured prediction, in particular structured support vector machines, results
in reduced prediction accuracy, or leads to infeasible optimization problems.
In this work we derive a maximum-margin training formulation for multi-label structured
prediction that remains computationally tractable while achieving high prediction
accuracy. It also shares most beneficial properties with single-label maximum-margin
approaches, in particular a formulation as a convex optimization problem, efficient
working set training, and PAC-Bayesian generalization bounds.
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. Maximum Margin Multi Label Structured Prediction. Neural
Information Processing Systems Foundation; 2011.
apa: 'Lampert, C. (2011). Maximum margin multi label structured prediction.
NIPS: Neural Information Processing Systems. Neural Information Processing
Systems Foundation.'
chicago: 'Lampert, Christoph. Maximum Margin Multi Label Structured Prediction.
NIPS: Neural Information Processing Systems. Neural Information Processing
Systems Foundation, 2011.'
ieee: C. Lampert, Maximum margin multi label structured prediction. Neural
Information Processing Systems Foundation, 2011.
ista: Lampert C. 2011. Maximum margin multi label structured prediction, Neural
Information Processing Systems Foundation,p.
mla: 'Lampert, Christoph. “Maximum Margin Multi Label Structured Prediction.” NIPS:
Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2011.'
short: C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information
Processing Systems Foundation, 2011.
date_created: 2018-12-11T12:02:40Z
date_published: 2011-12-13T00:00:00Z
date_updated: 2023-10-17T11:47:36Z
day: '13'
department:
- _id: ChLa
language:
- iso: eng
month: '12'
oa_version: None
publication: 'NIPS: Neural Information Processing Systems'
publication_status: published
publisher: Neural Information Processing Systems Foundation
publist_id: '3313'
related_material:
record:
- id: '3163'
relation: earlier_version
status: public
status: public
title: Maximum margin multi label structured prediction
type: conference_poster
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3320'
abstract:
- lang: eng
text: Powerful statistical models that can be learned efficiently from large amounts
of data are currently revolutionizing computer vision. These models possess a
rich internal structure reflecting task-specific relations and constraints. This
monograph introduces the reader to the most popular classes of structured models
in computer vision. Our focus is discrete undirected graphical models which we
cover in detail together with a description of algorithms for both probabilistic
inference and maximum a posteriori inference. We discuss separately recently successful
techniques for prediction in general structured models. In the second part of
this monograph we describe methods for parameter learning where we distinguish
the classic maximum likelihood based methods from the more recent prediction-based
parameter learning methods. We highlight developments to enhance current models
and discuss kernelized models and latent variable models. To make the monograph
more practical and to provide links to further study we provide examples of successful
application of many methods in the computer vision literature.
article_processing_charge: No
article_type: original
author:
- 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: Nowozin S, Lampert C. Structured learning and prediction in computer vision.
Foundations and Trends in Computer Graphics and Vision. 2011;6(3-4):185-365.
doi:10.1561/0600000033
apa: Nowozin, S., & Lampert, C. (2011). Structured learning and prediction in
computer vision. Foundations and Trends in Computer Graphics and Vision.
Now Publishers. https://doi.org/10.1561/0600000033
chicago: Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction
in Computer Vision.” Foundations and Trends in Computer Graphics and Vision.
Now Publishers, 2011. https://doi.org/10.1561/0600000033.
ieee: S. Nowozin and C. Lampert, “Structured learning and prediction in computer
vision,” Foundations and Trends in Computer Graphics and Vision, vol. 6,
no. 3–4. Now Publishers, pp. 185–365, 2011.
ista: Nowozin S, Lampert C. 2011. Structured learning and prediction in computer
vision. Foundations and Trends in Computer Graphics and Vision. 6(3–4), 185–365.
mla: Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction
in Computer Vision.” Foundations and Trends in Computer Graphics and Vision,
vol. 6, no. 3–4, Now Publishers, 2011, pp. 185–365, doi:10.1561/0600000033.
short: S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision
6 (2011) 185–365.
date_created: 2018-12-11T12:02:39Z
date_published: 2011-05-23T00:00:00Z
date_updated: 2023-10-17T11:52:46Z
day: '23'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1561/0600000033
file:
- access_level: open_access
checksum: f1043ef389f1558e2a226bb51568511f
content_type: application/pdf
creator: dernst
date_created: 2020-05-14T14:34:47Z
date_updated: 2020-07-14T12:46:07Z
file_id: '7837'
file_name: 2011_CompGraphicsVision_Nowozin.pdf
file_size: 3745064
relation: main_file
file_date_updated: 2020-07-14T12:46:07Z
has_accepted_license: '1'
intvolume: ' 6'
issue: 3-4
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 185 - 365
publication: Foundations and Trends in Computer Graphics and Vision
publication_status: published
publisher: Now Publishers
publist_id: '3315'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Structured learning and prediction in computer vision
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2011'
...
---
_id: '3319'
abstract:
- lang: eng
text: We address the problem of metric learning for multi-view data, namely the
construction of embedding projections from data in different representations into
a shared feature space, such that the Euclidean distance in this space provides
a meaningful within-view as well as between-view similarity. Our motivation stems
from the problem of cross-media retrieval tasks, where the availability of a joint
Euclidean distance function is a pre-requisite to allow fast, in particular hashing-based,
nearest neighbor queries. We formulate an objective function that expresses the
intuitive concept that matching samples are mapped closely together in the output
space, whereas non-matching samples are pushed apart, no matter in which view
they are available. The resulting optimization problem is not convex, but it can
be decomposed explicitly into a convex and a concave part, thereby allowing efficient
optimization using the convex-concave procedure. Experiments on an image retrieval
task show that nearest-neighbor based cross-view retrieval is indeed possible,
and the proposed technique improves the retrieval accuracy over baseline techniques.
article_processing_charge: No
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. Learning multi-view neighborhood preserving projections.
In: ML Research Press; 2011:425-432.'
apa: 'Quadrianto, N., & Lampert, C. (2011). Learning multi-view neighborhood
preserving projections (pp. 425–432). Presented at the ICML: International Conference
on Machine Learning, Bellevue, United States: ML Research Press.'
chicago: Quadrianto, Novi, and Christoph Lampert. “Learning Multi-View Neighborhood
Preserving Projections,” 425–32. ML Research Press, 2011.
ieee: 'N. Quadrianto and C. Lampert, “Learning multi-view neighborhood preserving
projections,” presented at the ICML: International Conference on Machine Learning,
Bellevue, United States, 2011, pp. 425–432.'
ista: 'Quadrianto N, Lampert C. 2011. Learning multi-view neighborhood preserving
projections. ICML: International Conference on Machine Learning, 425–432.'
mla: Quadrianto, Novi, and Christoph Lampert. Learning Multi-View Neighborhood
Preserving Projections. ML Research Press, 2011, pp. 425–32.
short: N. Quadrianto, C. Lampert, in:, ML Research Press, 2011, pp. 425–432.
conference:
end_date: 2011-07-02
location: Bellevue, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2011-06-28
date_created: 2018-12-11T12:02:39Z
date_published: 2011-01-01T00:00:00Z
date_updated: 2023-10-17T11:59:50Z
day: '01'
department:
- _id: ChLa
language:
- iso: eng
month: '01'
oa_version: None
page: 425 - 432
publication_status: published
publisher: ML Research Press
publist_id: '3316'
scopus_import: '1'
status: public
title: Learning multi-view neighborhood preserving projections
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2011'
...
---
_id: '3794'
abstract:
- lang: eng
text: 'We study the problem of multimodal dimensionality reduction assuming that
data samples can be missing at training time, and not all data modalities may
be present at application time. Maximum covariance analysis, as a generalization
of PCA, has many desirable properties, but its application to practical problems
is limited by its need for perfectly paired data. We overcome this limitation
by a latent variable approach that allows working with weakly paired data and
is still able to efficiently process large datasets using standard numerical routines.
The resulting weakly paired maximum covariance analysis often finds better representations
than alternative methods, as we show in two exemplary tasks: texture discrimination
and transfer learning.'
alternative_title:
- LNCS
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Oliver
full_name: Krömer, Oliver
last_name: Krömer
citation:
ama: 'Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal
dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579.
doi:10.1007/978-3-642-15552-9_41'
apa: 'Lampert, C., & Krömer, O. (2010). Weakly-paired maximum covariance analysis
for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp.
566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion,
Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15552-9_41'
chicago: Lampert, Christoph, and Oliver Krömer. “Weakly-Paired Maximum Covariance
Analysis for Multimodal Dimensionality Reduction and Transfer Learning,” 6312:566–79.
Springer, 2010. https://doi.org/10.1007/978-3-642-15552-9_41.
ieee: 'C. Lampert and O. Krömer, “Weakly-paired maximum covariance analysis for
multimodal dimensionality reduction and transfer learning,” presented at the ECCV:
European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6312,
pp. 566–579.'
ista: 'Lampert C, Krömer O. 2010. Weakly-paired maximum covariance analysis for
multimodal dimensionality reduction and transfer learning. ECCV: European Conference
on Computer Vision, LNCS, vol. 6312, 566–579.'
mla: Lampert, Christoph, and Oliver Krömer. Weakly-Paired Maximum Covariance
Analysis for Multimodal Dimensionality Reduction and Transfer Learning. Vol.
6312, Springer, 2010, pp. 566–79, doi:10.1007/978-3-642-15552-9_41.
short: C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.
conference:
end_date: 2010-09-11
location: Heraklion, Crete, Greece
name: 'ECCV: European Conference on Computer Vision'
start_date: 2010-09-05
date_created: 2018-12-11T12:05:12Z
date_published: 2010-11-10T00:00:00Z
date_updated: 2021-01-12T07:52:14Z
day: '10'
department:
- _id: ChLa
doi: 10.1007/978-3-642-15552-9_41
intvolume: ' 6312'
language:
- iso: eng
main_file_link:
- url: http://www.ics.forth.gr/eccv2010/intro.php
month: '11'
oa_version: None
page: 566 - 579
publication_status: published
publisher: Springer
publist_id: '2433'
quality_controlled: '1'
scopus_import: 1
status: public
title: Weakly-paired maximum covariance analysis for multimodal dimensionality reduction
and transfer learning
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 6312
year: '2010'
...
---
_id: '3793'
abstract:
- lang: eng
text: "Recent progress in per-pixel object class labeling of natural images can
be attributed to the use of multiple types of image features and sound statistical
learning approaches. Within the latter, Conditional Random Fields (CRF) are prominently
used for their ability to represent interactions between random variables. Despite
their popularity in computer vision, parameter learning for CRFs has remained
difficult, popular approaches being cross-validation and piecewise training.\r\nIn
this work, we propose a simple yet expressive tree-structured CRF based on a recent
hierarchical image segmentation method. Our model combines and weights multiple
image features within a hierarchical representation and allows simple and efficient
globally-optimal learning of ≈ 105 parameters. The tractability of our model allows
us to pose and answer some of the open questions regarding parameter learning
applying to CRF-based approaches. The key findings for learning CRF models are,
from the obvious to the surprising, i) multiple image features always help, ii)
the limiting dimension with respect to current models is the amount of training
data, iii) piecewise training is competitive, iv) current methods for max-margin
training fail for models with many parameters.\r\n"
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Sebastian
full_name: Nowozin, Sebastian
last_name: Nowozin
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Nowozin S, Gehler P, Lampert C. On parameter learning in CRF-based approaches
to object class image segmentation. In: Vol 6316. Springer; 2010:98-111. doi:10.1007/978-3-642-15567-3_8'
apa: 'Nowozin, S., Gehler, P., & Lampert, C. (2010). On parameter learning in
CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111).
Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete,
Greece: Springer. https://doi.org/10.1007/978-3-642-15567-3_8'
chicago: Nowozin, Sebastian, Peter Gehler, and Christoph Lampert. “On Parameter
Learning in CRF-Based Approaches to Object Class Image Segmentation,” 6316:98–111.
Springer, 2010. https://doi.org/10.1007/978-3-642-15567-3_8.
ieee: 'S. Nowozin, P. Gehler, and C. Lampert, “On parameter learning in CRF-based
approaches to object class image segmentation,” presented at the ECCV: European
Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6316, pp.
98–111.'
ista: 'Nowozin S, Gehler P, Lampert C. 2010. On parameter learning in CRF-based
approaches to object class image segmentation. ECCV: European Conference on Computer
Vision, LNCS, vol. 6316, 98–111.'
mla: Nowozin, Sebastian, et al. On Parameter Learning in CRF-Based Approaches
to Object Class Image Segmentation. Vol. 6316, Springer, 2010, pp. 98–111,
doi:10.1007/978-3-642-15567-3_8.
short: S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111.
conference:
end_date: 2010-09-11
location: Heraklion, Crete, Greece
name: 'ECCV: European Conference on Computer Vision'
start_date: 2010-09-05
date_created: 2018-12-11T12:05:12Z
date_published: 2010-11-04T00:00:00Z
date_updated: 2021-01-12T07:52:14Z
day: '04'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-15567-3_8
file:
- access_level: open_access
checksum: 3716e10e161f7c714fd17ec193a223c3
content_type: application/pdf
creator: dernst
date_created: 2020-05-19T16:27:34Z
date_updated: 2020-07-14T12:46:16Z
file_id: '7871'
file_name: 2010_ECCV_Nowozin.pdf
file_size: 4087332
relation: main_file
file_date_updated: 2020-07-14T12:46:16Z
has_accepted_license: '1'
intvolume: ' 6316'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Submitted Version
page: 98 - 111
publication_status: published
publisher: Springer
publist_id: '2431'
quality_controlled: '1'
scopus_import: 1
status: public
title: On parameter learning in CRF-based approaches to object class image segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6316
year: '2010'
...