---
_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
license: https://creativecommons.org/licenses/by/4.0/
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'
...