---
_id: '1098'
abstract:
- lang: eng
text: Better understanding of the potential benefits of information transfer and
representation learning is an important step towards the goal of building intelligent
systems that are able to persist in the world and learn over time. In this work,
we consider a setting where the learner encounters a stream of tasks but is able
to retain only limited information from each encountered task, such as a learned
predictor. In contrast to most previous works analyzing this scenario, we do not
make any distributional assumptions on the task generating process. Instead, we
formulate a complexity measure that captures the diversity of the observed tasks.
We provide a lifelong learning algorithm with error guarantees for every observed
task (rather than on average). We show sample complexity reductions in comparison
to solving every task in isolation in terms of our task complexity measure. Further,
our algorithmic framework can naturally be viewed as learning a representation
from encountered tasks with a neural network.
acknowledgement: "This work was in parts funded by the European Research Council under
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement
no 308036.\r\n\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Ruth
full_name: Urner, Ruth
last_name: Urner
citation:
ama: 'Pentina A, Urner R. Lifelong learning with weighted majority votes. In: Vol
29. Neural Information Processing Systems; 2016:3619-3627.'
apa: 'Pentina, A., & Urner, R. (2016). Lifelong learning with weighted majority
votes (Vol. 29, pp. 3619–3627). Presented at the NIPS: Neural Information Processing
Systems, Barcelona, Spain: Neural Information Processing Systems.'
chicago: Pentina, Anastasia, and Ruth Urner. “Lifelong Learning with Weighted Majority
Votes,” 29:3619–27. Neural Information Processing Systems, 2016.
ieee: 'A. Pentina and R. Urner, “Lifelong learning with weighted majority votes,”
presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain,
2016, vol. 29, pp. 3619–3627.'
ista: 'Pentina A, Urner R. 2016. Lifelong learning with weighted majority votes.
NIPS: Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 29, 3619–3627.'
mla: Pentina, Anastasia, and Ruth Urner. Lifelong Learning with Weighted Majority
Votes. Vol. 29, Neural Information Processing Systems, 2016, pp. 3619–27.
short: A. Pentina, R. Urner, in:, Neural Information Processing Systems, 2016, pp.
3619–3627.
conference:
end_date: 2016-12-10
location: Barcelona, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:08Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:15Z
day: '01'
ddc:
- '006'
department:
- _id: ChLa
ec_funded: 1
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:42Z
date_updated: 2018-12-12T10:12:42Z
file_id: '4961'
file_name: IST-2017-775-v1+1_main.pdf
file_size: 237111
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:43Z
date_updated: 2018-12-12T10:12:43Z
file_id: '4962'
file_name: IST-2017-775-v1+2_supplementary.pdf
file_size: 185818
relation: main_file
file_date_updated: 2018-12-12T10:12:43Z
has_accepted_license: '1'
intvolume: ' 29'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 3619-3627
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6277'
pubrep_id: '775'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lifelong learning with weighted majority votes
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1102'
abstract:
- lang: eng
text: Weakly-supervised object localization methods tend to fail for object classes
that consistently co-occur with the same background elements, e.g. trains on tracks.
We propose a method to overcome these failures by adding a very small amount of
model-specific additional annotation. The main idea is to cluster a deep network\'s
mid-level representations and assign object or distractor labels to each cluster.
Experiments show substantially improved localization results on the challenging
ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for
semantic segmentation.
acknowledgement: "This work was funded in parts by the European Research Council\r\nunder
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant\r\nagreement
no 308036. We gratefully acknowledge the support of NVIDIA Corporation with\r\nthe
donation of the GPUs used for this research."
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Improving weakly-supervised object localization by
micro-annotation. In: Proceedings of the British Machine Vision Conference
2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object
localization by micro-annotation. In Proceedings of the British Machine Vision
Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom:
BMVA Press. https://doi.org/10.5244/C.30.92'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” In Proceedings of the British Machine
Vision Conference 2016, 2016–September:92.1-92.12. BMVA Press, 2016. https://doi.org/10.5244/C.30.92.
ieee: A. Kolesnikov and C. Lampert, “Improving weakly-supervised object localization
by micro-annotation,” in Proceedings of the British Machine Vision Conference
2016, York, United Kingdom, 2016, vol. 2016–September, p. 92.1-92.12.
ista: 'Kolesnikov A, Lampert C. 2016. Improving weakly-supervised object localization
by micro-annotation. Proceedings of the British Machine Vision Conference 2016.
BMVC: British Machine Vision Conference vol. 2016–September, 92.1-92.12.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” Proceedings of the British Machine
Vision Conference 2016, vol. 2016–September, BMVA Press, 2016, p. 92.1-92.12,
doi:10.5244/C.30.92.
short: A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision
Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
conference:
end_date: 2016-09-22
location: York, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2016-09-19
date_created: 2018-12-11T11:50:09Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T06:48:18Z
day: '01'
department:
- _id: ChLa
doi: 10.5244/C.30.92
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.bmva.org/bmvc/2016/papers/paper092/paper092.pdf
month: '09'
oa: 1
oa_version: Published Version
page: 92.1-92.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2016
publication_status: published
publisher: BMVA Press
publist_id: '6273'
quality_controlled: '1'
scopus_import: 1
status: public
title: Improving weakly-supervised object localization by micro-annotation
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-September
year: '2016'
...
---
_id: '1126'
abstract:
- lang: eng
text: "Traditionally machine learning has been focusing on the problem of solving
a single\r\ntask in isolation. While being quite well understood, this approach
disregards an\r\nimportant aspect of human learning: when facing a new problem,
humans are able to\r\nexploit knowledge acquired from previously learned tasks.
Intuitively, access to several\r\nproblems simultaneously or sequentially could
also be advantageous for a machine\r\nlearning system, especially if these tasks
are closely related. Indeed, results of many\r\nempirical studies have provided
justification for this intuition. However, theoretical\r\njustifications of this
idea are rather limited.\r\nThe focus of this thesis is to expand the understanding
of potential benefits of information\r\ntransfer between several related learning
problems. We provide theoretical\r\nanalysis for three scenarios of multi-task
learning - multiple kernel learning, sequential\r\nlearning and active task selection.
We also provide a PAC-Bayesian perspective on\r\nlifelong learning and investigate
how the task generation process influences the generalization\r\nguarantees in
this scenario. In addition, we show how some of the obtained\r\ntheoretical results
can be used to derive principled multi-task and lifelong learning\r\nalgorithms
and illustrate their performance on various synthetic and real-world datasets."
acknowledgement: "First and foremost I would like to express my gratitude to my supervisor,
Christoph\r\nLampert. Thank you for your patience in teaching me all aspects of
doing research\r\n(including English grammar), for your trust in my capabilities
and endless support. Thank\r\nyou for granting me freedom in my research and, at
the same time, having time and\r\nhelping me cope with the consequences whenever
I needed it. Thank you for creating\r\nan excellent atmosphere in the group, it
was a great pleasure and honor to be a part of\r\nit. There could not have been
a better and more inspiring adviser and mentor.\r\nI thank Shai Ben-David for welcoming
me into his group at the University of Waterloo,\r\nfor inspiring discussions and
support. It was a great pleasure to work together. I am\r\nalso thankful to Ruth
Urner for hosting me at the Max-Planck Institute Tübingen, for the\r\nfruitful
collaboration and for taking care of me during that not-so-sunny month of May.\r\nI
thank Jan Maas for kindly joining my thesis committee despite the short notice and\r\nproviding
me with insightful comments.\r\nI would like to thank my colleagues for their support,
entertaining conversations and\r\nendless table soccer games we shared together:
Georg, Jan, Amelie and Emilie, Michal\r\nand Alex, Alex K. and Alex Z., Thomas,
Sameh, Vlad, Mayu, Nathaniel, Silvester, Neel,\r\nCsaba, Vladimir, Morten. Thank
you, Mabel and Ram, for the wonderful time we spent\r\ntogether. I am thankful to
Shrinu and Samira for taking care of me during my stay at the\r\nUniversity of Waterloo.
Special thanks to Viktoriia for her never-ending optimism and for\r\nbeing so inspiring
and supportive, especially at the beginning of my PhD journey.\r\nThanks to IST
administration, in particular, Vlad and Elisabeth for shielding me from\r\nmost
of the bureaucratic paperwork.\r\n\r\nThis dissertation would not have been possible
without funding from the European\r\nResearch Council under the European Union's
Seventh Framework Programme\r\n(FP7/2007-2013)/ERC grant agreement no 308036."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
citation:
ama: Pentina A. Theoretical foundations of multi-task lifelong learning. 2016. doi:10.15479/AT:ISTA:TH_776
apa: Pentina, A. (2016). Theoretical foundations of multi-task lifelong learning.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH_776
chicago: Pentina, Anastasia. “Theoretical Foundations of Multi-Task Lifelong Learning.”
Institute of Science and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:TH_776.
ieee: A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute
of Science and Technology Austria, 2016.
ista: Pentina A. 2016. Theoretical foundations of multi-task lifelong learning.
Institute of Science and Technology Austria.
mla: Pentina, Anastasia. Theoretical Foundations of Multi-Task Lifelong Learning.
Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:TH_776.
short: A. Pentina, Theoretical Foundations of Multi-Task Lifelong Learning, Institute
of Science and Technology Austria, 2016.
date_created: 2018-12-11T11:50:17Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2023-09-07T11:52:03Z
day: '01'
ddc:
- '006'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH_776
ec_funded: 1
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:07Z
date_updated: 2018-12-12T10:14:07Z
file_id: '5056'
file_name: IST-2017-776-v1+1_Pentina_Thesis_2016.pdf
file_size: 2140062
relation: main_file
file_date_updated: 2018-12-12T10:14:07Z
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: '127'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6234'
pubrep_id: '776'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Theoretical foundations of multi-task lifelong learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2016'
...
---
_id: '8094'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. The idea is that the controller controls the world---the body plus
its environment---as reliably as possible. This paper focuses on new lines of
self-organization for developmental robotics. We apply the recently developed
differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder
system from the Myorobotics toolkit. In the experiments, we observe a vast variety
of self-organized behavior patterns: when left alone, the arm realizes pseudo-random
sequences of different poses. By applying physical forces, the system can be entrained
into definite motion patterns like wiping a table. Most interestingly, after attaching
an object, the controller gets in a functional resonance with the object''s internal
dynamics, starting to shake spontaneously bottles half-filled with water or sensitively
driving an attached pendulum into a circular mode. When attached to the crank
of a wheel the neural system independently discovers how to rotate it. In this
way, the robot discovers affordances of objects its body is interacting with.'
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Rafael
full_name: Hostettler, Rafael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon
driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial
Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized
control of an tendon driven arm by differential extrinsic plasticity. In Proceedings
of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico:
MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029'
chicago: Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized
Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In Proceedings
of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.
ieee: G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control
of an tendon driven arm by differential extrinsic plasticity,” in Proceedings
of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp.
142–143.
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of
an tendon driven arm by differential extrinsic plasticity. Proceedings of the
Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on
the Synthesis and Simulation of Living Systems vol. 28, 142–143.'
mla: Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by
Differential Extrinsic Plasticity.” Proceedings of the Artificial Life Conference
2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial
Life Conference 2016, MIT Press, 2016, pp. 142–143.
conference:
end_date: 2016-07-08
location: Cancun, Mexico
name: 'ALIFE 2016: 15th International Conference on the Synthesis and Simulation
of Living Systems'
start_date: 2016-07-04
date_created: 2020-07-05T22:00:47Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T08:16:53Z
day: '01'
ddc:
- '610'
department:
- _id: ChLa
- _id: GaTk
doi: 10.7551/978-0-262-33936-0-ch029
ec_funded: 1
file:
- access_level: open_access
checksum: cff63e7a4b8ac466ba51a9c84153a940
content_type: application/pdf
creator: cziletti
date_created: 2020-07-06T12:59:09Z
date_updated: 2020-07-14T12:48:09Z
file_id: '8096'
file_name: 2016_ProcALIFE_Martius.pdf
file_size: 678670
relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 142-143
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the Artificial Life Conference 2016
publication_identifier:
isbn:
- '9780262339360'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: 1
status: public
title: Self-organized control of an tendon driven arm by differential extrinsic plasticity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 28
year: '2016'
...
---
_id: '1707'
abstract:
- lang: eng
text: "Volunteer supporters play an important role in modern crisis and disaster
management. In the times of mobile Internet devices, help from thousands of volunteers
can be requested within a short time span, thus relieving professional helpers
from minor chores or geographically spread-out tasks. However, the simultaneous
availability of many volunteers also poses new problems. In particular, the volunteer
efforts must be well coordinated, or otherwise situations might emerge in which
too many idle volunteers at one location become more of a burden than a relief
to the professionals.\r\nIn this work, we study the task of optimally assigning
volunteers to selected locations, e.g. in order to perform regular measurements,
to report on damage, or to distribute information or resources to the population
in a crisis situation. We formulate the assignment tasks as an optimization problem
and propose an effective and efficient solution procedure. Experiments on real
data of the Team Österreich, consisting of over 36,000 Austrian volunteers, show
the effectiveness and efficiency of our approach."
acknowledgement: The DRIVER FP7 project has received funding from the European Unions
Seventh Framework Programme for research, technological development and demonstration
under grant agreement no 607798. RE-ACTA was funded within the framework of the
Austrian Security Research Programme KIRAS by the Federal Ministry for Transport,
Innovation and Technology.
article_number: '7402041'
author:
- first_name: Jasmin
full_name: Pielorz, Jasmin
id: 49BC895A-F248-11E8-B48F-1D18A9856A87
last_name: Pielorz
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pielorz J, Lampert C. Optimal geospatial allocation of volunteers for crisis
management. In: IEEE; 2016. doi:10.1109/ICT-DM.2015.7402041'
apa: 'Pielorz, J., & Lampert, C. (2016). Optimal geospatial allocation of volunteers
for crisis management. Presented at the ICT-DM: Information and Communication
Technologies for Disaster Management, Rennes, France: IEEE. https://doi.org/10.1109/ICT-DM.2015.7402041'
chicago: Pielorz, Jasmin, and Christoph Lampert. “Optimal Geospatial Allocation
of Volunteers for Crisis Management.” IEEE, 2016. https://doi.org/10.1109/ICT-DM.2015.7402041.
ieee: 'J. Pielorz and C. Lampert, “Optimal geospatial allocation of volunteers for
crisis management,” presented at the ICT-DM: Information and Communication Technologies
for Disaster Management, Rennes, France, 2016.'
ista: 'Pielorz J, Lampert C. 2016. Optimal geospatial allocation of volunteers for
crisis management. ICT-DM: Information and Communication Technologies for Disaster
Management, 7402041.'
mla: Pielorz, Jasmin, and Christoph Lampert. Optimal Geospatial Allocation of
Volunteers for Crisis Management. 7402041, IEEE, 2016, doi:10.1109/ICT-DM.2015.7402041.
short: J. Pielorz, C. Lampert, in:, IEEE, 2016.
conference:
end_date: 2015-12-02
location: Rennes, France
name: 'ICT-DM: Information and Communication Technologies for Disaster Management'
start_date: 2015-11-30
date_created: 2018-12-11T11:53:35Z
date_published: 2016-02-11T00:00:00Z
date_updated: 2021-01-12T06:52:39Z
day: '11'
department:
- _id: ChLa
doi: 10.1109/ICT-DM.2015.7402041
language:
- iso: eng
month: '02'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '5429'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optimal geospatial allocation of volunteers for crisis management
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2016'
...
---
_id: '1369'
abstract:
- lang: eng
text: 'We introduce a new loss function for the weakly-supervised training of semantic
image segmentation models based on three guiding principles: to seed with weak
localization cues, to expand objects based on the information about which classes
can occur in an image, and to constrain the segmentations to coincide with object
boundaries. We show experimentally that training a deep convolutional neural network
using the proposed loss function leads to substantially better segmentations than
previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset.
We furthermore give insight into the working mechanism of our method by a detailed
experimental study that illustrates how the segmentation quality is affected by
each term of the proposed loss function as well as their combinations.'
alternative_title:
- LNCS
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for
weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three
principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711).
Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The
Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42'
chicago: 'Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation,” 9908:695–711. Springer,
2016. https://doi.org/10.1007/978-3-319-46493-0_42.'
ieee: 'A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles
for weakly-supervised image segmentation,” presented at the ECCV: European Conference
on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.'
ista: 'Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles
for weakly-supervised image segmentation. ECCV: European Conference on Computer
Vision, LNCS, vol. 9908, 695–711.'
mla: 'Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer,
2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.'
short: A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
conference:
end_date: 2016-10-14
location: Amsterdam, The Netherlands
name: 'ECCV: European Conference on Computer Vision'
start_date: 2016-10-11
date_created: 2018-12-11T11:51:37Z
date_published: 2016-09-15T00:00:00Z
date_updated: 2021-01-12T06:50:12Z
day: '15'
department:
- _id: ChLa
doi: 10.1007/978-3-319-46493-0_42
ec_funded: 1
intvolume: ' 9908'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1603.06098
month: '09'
oa: 1
oa_version: Preprint
page: 695 - 711
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5842'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Seed, expand and constrain: Three principles for weakly-supervised image segmentation'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 9908
year: '2016'
...
---
_id: '1214'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. While very successful with classical robots, these methods run into
severe difficulties when applied to soft robots, a new field of robotics with
large interest for human-robot interaction. We claim that a novel controller paradigm
opens new perspective for this field. This paper applies a recently developed
neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon
driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we
observe a vast variety of self-organized behavior patterns: when left alone, the
arm realizes pseudo-random sequences of different poses. By applying physical
forces, the system can be entrained into definite motion patterns like wiping
a table. Most interestingly, after attaching an object, the controller gets in
a functional resonance with the object''s internal dynamics, starting to shake
spontaneously bottles half-filled with water or sensitively driving an attached
pendulum into a circular mode. When attached to the crank of a wheel the neural
system independently develops to rotate it. In this way, the robot discovers affordances
of objects its body is interacting with.'
acknowledgement: RD thanks for the hospitality at the Max-Planck-Institute and for
helpful discussions with Nihat Ay and Keyan Zahedi.
article_number: '7759138'
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Raphael
full_name: Hostettler, Raphael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots:
Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November.
IEEE; 2016. doi:10.1109/IROS.2016.7759138'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant
control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on
Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138'
chicago: 'Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant
Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic
Arm,” Vol. 2016–November. IEEE, 2016. https://doi.org/10.1109/IROS.2016.7759138.'
ieee: 'G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for
soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented
at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS
, Daejeon, Korea, 2016, vol. 2016–November.'
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft
robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International
Conference on Intelligent Robots and Systems IROS vol. 2016–November, 7759138.'
mla: 'Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior
of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE,
2016, doi:10.1109/IROS.2016.7759138.'
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.
conference:
end_date: 2016-09-14
location: Daejeon, Korea
name: 'IEEE RSJ International Conference on Intelligent Robots and Systems IROS '
start_date: 2016-09-09
date_created: 2018-12-11T11:50:45Z
date_published: 2016-11-28T00:00:00Z
date_updated: 2021-01-12T06:49:08Z
day: '28'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/IROS.2016.7759138
language:
- iso: eng
month: '11'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '6121'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-November
year: '2016'
...
---
_id: '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: '1706'
abstract:
- lang: eng
text: We consider a problem of learning kernels for use in SVM classification in
the multi-task and lifelong scenarios and provide generalization bounds on the
error of a large margin classifier. Our results show that, under mild conditions
on the family of kernels used for learning, solving several related tasks simultaneously
is beneficial over single task learning. In particular, as the number of observed
tasks grows, assuming that in the considered family of kernels there exists one
that yields low approximation error on all tasks, the overhead associated with
learning such a kernel vanishes and the complexity converges to that of learning
when this good kernel is given to the learner.
alternative_title:
- LNCS
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Shai
full_name: Ben David, Shai
last_name: Ben David
citation:
ama: 'Pentina A, Ben David S. Multi-task and lifelong learning of kernels. In: Vol
9355. Springer; 2015:194-208. doi:10.1007/978-3-319-24486-0_13'
apa: 'Pentina, A., & Ben David, S. (2015). Multi-task and lifelong learning
of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning
Theory, Banff, AB, Canada: Springer. https://doi.org/10.1007/978-3-319-24486-0_13'
chicago: Pentina, Anastasia, and Shai Ben David. “Multi-Task and Lifelong Learning
of Kernels,” 9355:194–208. Springer, 2015. https://doi.org/10.1007/978-3-319-24486-0_13.
ieee: 'A. Pentina and S. Ben David, “Multi-task and lifelong learning of kernels,”
presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada, 2015, vol.
9355, pp. 194–208.'
ista: 'Pentina A, Ben David S. 2015. Multi-task and lifelong learning of kernels.
ALT: Algorithmic Learning Theory, LNCS, vol. 9355, 194–208.'
mla: Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning
of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.
short: A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208.
conference:
end_date: 2015-10-06
location: Banff, AB, Canada
name: 'ALT: Algorithmic Learning Theory'
start_date: 2015-10-04
date_created: 2018-12-11T11:53:35Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:52:39Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-24486-0_13
ec_funded: 1
intvolume: ' 9355'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1602.06531
month: '01'
oa: 1
oa_version: Preprint
page: 194 - 208
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5430'
quality_controlled: '1'
scopus_import: 1
status: public
title: Multi-task and lifelong learning of kernels
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9355
year: '2015'
...
---
_id: '1857'
abstract:
- lang: eng
text: 'Sharing information between multiple tasks enables algorithms to achieve
good generalization performance even from small amounts of training data. However,
in a realistic scenario of multi-task learning not all tasks are equally related
to each other, hence it could be advantageous to transfer information only between
the most related tasks. In this work we propose an approach that processes multiple
tasks in a sequence with sharing between subsequent tasks instead of solving all
tasks jointly. Subsequently, we address the question of curriculum learning of
tasks, i.e. finding the best order of tasks to be learned. Our approach is based
on a generalization bound criterion for choosing the task order that optimizes
the average expected classification performance over all tasks. Our experimental
results show that learning multiple related tasks sequentially can be more effective
than learning them jointly, the order in which tasks are being solved affects
the overall performance, and that our model is able to automatically discover
the favourable order of tasks. '
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Sharmanska V, Lampert C. Curriculum learning of multiple tasks.
In: IEEE; 2015:5492-5500. doi:10.1109/CVPR.2015.7299188'
apa: 'Pentina, A., Sharmanska, V., & Lampert, C. (2015). Curriculum learning
of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and
Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7299188'
chicago: Pentina, Anastasia, Viktoriia Sharmanska, and Christoph Lampert. “Curriculum
Learning of Multiple Tasks,” 5492–5500. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7299188.
ieee: 'A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple
tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston,
MA, United States, 2015, pp. 5492–5500.'
ista: 'Pentina A, Sharmanska V, Lampert C. 2015. Curriculum learning of multiple
tasks. CVPR: Computer Vision and Pattern Recognition, 5492–5500.'
mla: Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE,
2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188.
short: A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.
conference:
end_date: 2015-06-12
location: Boston, MA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:23Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2023-02-23T10:17:31Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7299188
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1412.1353
month: '06'
oa: 1
oa_version: Preprint
page: 5492 - 5500
publication_status: published
publisher: IEEE
publist_id: '5243'
quality_controlled: '1'
scopus_import: 1
status: public
title: Curriculum learning of multiple tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1858'
abstract:
- lang: eng
text: 'We study the problem of predicting the future, though only in the probabilistic
sense of estimating a future state of a time-varying probability distribution.
This is not only an interesting academic problem, but solving this extrapolation
problem also has many practical application, e.g. for training classifiers that
have to operate under time-varying conditions. Our main contribution is a method
for predicting the next step of the time-varying distribution from a given sequence
of sample sets from earlier time steps. For this we rely on two recent machine
learning techniques: embedding probability distributions into a reproducing kernel
Hilbert space, and learning operators by vector-valued regression. We illustrate
the working principles and the practical usefulness of our method by experiments
on synthetic and real data. We also highlight an exemplary application: training
a classifier in a domain adaptation setting without having access to examples
from the test time distribution at training time.'
arxiv: 1
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Predicting the future behavior of a time-varying probability distribution.
In: IEEE; 2015:942-950. doi:10.1109/CVPR.2015.7298696'
apa: 'Lampert, C. (2015). Predicting the future behavior of a time-varying probability
distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern
Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298696'
chicago: Lampert, Christoph. “Predicting the Future Behavior of a Time-Varying Probability
Distribution,” 942–50. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298696.
ieee: 'C. Lampert, “Predicting the future behavior of a time-varying probability
distribution,” presented at the CVPR: Computer Vision and Pattern Recognition,
Boston, MA, United States, 2015, pp. 942–950.'
ista: 'Lampert C. 2015. Predicting the future behavior of a time-varying probability
distribution. CVPR: Computer Vision and Pattern Recognition, 942–950.'
mla: Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability
Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.
short: C. Lampert, in:, IEEE, 2015, pp. 942–950.
conference:
end_date: 2015-06-12
location: Boston, MA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:24Z
date_published: 2015-10-15T00:00:00Z
date_updated: 2021-01-12T06:53:40Z
day: '15'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7298696
external_id:
arxiv:
- '1406.5362'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1406.5362
month: '10'
oa: 1
oa_version: Preprint
page: 942 - 950
publication_status: published
publisher: IEEE
publist_id: '5241'
quality_controlled: '1'
scopus_import: 1
status: public
title: Predicting the future behavior of a time-varying probability distribution
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1859'
abstract:
- lang: eng
text: "Structural support vector machines (SSVMs) are amongst the best performing
models for structured computer vision tasks, such as semantic image segmentation
or human pose estimation. Training SSVMs, however, is computationally costly,
because it requires repeated calls to a structured prediction subroutine (called
\\emph{max-oracle}), which has to solve an optimization problem itself, e.g. a
graph cut.\r\nIn this work, we introduce a new algorithm for SSVM training that
is more efficient than earlier techniques when the max-oracle is computationally
expensive, as it is frequently the case in computer vision tasks. The main idea
is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm
with efficient hyperplane caching, and (ii) use an automatic selection rule for
deciding whether to call the exact max-oracle or to rely on an approximate one
based on the cached hyperplanes.\r\nWe show experimentally that this strategy
leads to faster convergence to the optimum with respect to the number of requires
oracle calls, and that this translates into faster convergence with respect to
the total runtime when the max-oracle is slow compared to the other steps of the
algorithm. "
author:
- first_name: Neel
full_name: Shah, Neel
id: 31ABAF80-F248-11E8-B48F-1D18A9856A87
last_name: Shah
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Shah N, Kolmogorov V, Lampert C. A multi-plane block-coordinate Frank-Wolfe
algorithm for training structural SVMs with a costly max-oracle. In: IEEE; 2015:2737-2745.
doi:10.1109/CVPR.2015.7298890'
apa: 'Shah, N., Kolmogorov, V., & Lampert, C. (2015). A multi-plane block-coordinate
Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp.
2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston,
MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7298890'
chicago: Shah, Neel, Vladimir Kolmogorov, and Christoph Lampert. “A Multi-Plane
Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly
Max-Oracle,” 2737–45. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298890.
ieee: 'N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe
algorithm for training structural SVMs with a costly max-oracle,” presented at
the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp.
2737–2745.'
ista: 'Shah N, Kolmogorov V, Lampert C. 2015. A multi-plane block-coordinate Frank-Wolfe
algorithm for training structural SVMs with a costly max-oracle. CVPR: Computer
Vision and Pattern Recognition, 2737–2745.'
mla: Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm
for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45,
doi:10.1109/CVPR.2015.7298890.
short: N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.
conference:
end_date: 2015-06-12
location: Boston, MA, USA
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:24Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2021-01-12T06:53:40Z
day: '01'
department:
- _id: VlKo
- _id: ChLa
doi: 10.1109/CVPR.2015.7298890
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1408.6804
month: '06'
oa: 1
oa_version: Preprint
page: 2737 - 2745
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication_status: published
publisher: IEEE
publist_id: '5240'
quality_controlled: '1'
scopus_import: 1
status: public
title: A multi-plane block-coordinate Frank-Wolfe algorithm for training structural
SVMs with a costly max-oracle
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1860'
abstract:
- lang: eng
text: Classifiers for object categorization are usually evaluated by their accuracy
on a set of i.i.d. test examples. This provides us with an estimate of the expected
error when applying the classifiers to a single new image. In real application,
however, classifiers are rarely only used for a single image and then discarded.
Instead, they are applied sequentially to many images, and these are typically
not i.i.d. samples from a fixed data distribution, but they carry dependencies
and their class distribution varies over time. In this work, we argue that the
phenomenon of correlated data at prediction time is not a nuisance, but a blessing
in disguise. We describe a probabilistic method for adapting classifiers at prediction
time without having to retrain them. We also introduce a framework for creating
realistically distributed image sequences, which offers a way to benchmark classifier
adaptation methods, such as the one we propose. Experiments on the ILSVRC2010
and ILSVRC2012 datasets show that adapting object classification systems at prediction
time can significantly reduce their error rate, even with no additional human
feedback.
author:
- first_name: Amélie
full_name: Royer, Amélie
last_name: Royer
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. Classifier adaptation at prediction time. In: IEEE; 2015:1401-1409.
doi:10.1109/CVPR.2015.7298746'
apa: 'Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time
(pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition,
Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746'
chicago: Royer, Amélie, and Christoph Lampert. “Classifier Adaptation at Prediction
Time,” 1401–9. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298746.
ieee: 'A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented
at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States,
2015, pp. 1401–1409.'
ista: 'Royer A, Lampert C. 2015. Classifier adaptation at prediction time. CVPR:
Computer Vision and Pattern Recognition, 1401–1409.'
mla: Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction
Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.
short: A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.
conference:
end_date: 2015-06-12
location: Boston, MA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2015-06-07
date_created: 2018-12-11T11:54:24Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2021-01-12T06:53:41Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7298746
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Royer_Classifier_Adaptation_at_2015_CVPR_paper.pdf
month: '06'
oa: 1
oa_version: Submitted Version
page: 1401 - 1409
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '5239'
quality_controlled: '1'
scopus_import: 1
status: public
title: Classifier adaptation at prediction time
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1533'
abstract:
- lang: eng
text: This paper addresses the problem of semantic segmentation, where the possible
class labels are from a predefined set. We exploit top-down guidance, i.e., the
coarse localization of the objects and their class labels provided by object detectors.
For each detected bounding box, figure-ground segmentation is performed and the
final result is achieved by merging the figure-ground segmentations. The main
idea of the proposed approach, which is presented in our preliminary work, is
to reformulate the figure-ground segmentation problem as sparse reconstruction
pursuing the object mask in a nonparametric manner. The latent segmentation mask
should be coherent subject to sparse error caused by intra-category diversity;
thus, the object mask is inferred by making use of sparse representations over
the training set. To handle local spatial deformations, local patch-level masks
are also considered and inferred by sparse representations over the spatially
nearby patches. The sparse reconstruction coefficients and the latent mask are
alternately optimized by applying the Lasso algorithm and the accelerated proximal
gradient method. The proposed formulation results in a convex optimization problem;
thus, the global optimal solution is achieved. In this paper, we provide theoretical
analysis of the convergence and optimality. We also give an extended numerical
analysis of the proposed algorithm and a comprehensive comparison with the related
semantic segmentation methods on the challenging PASCAL visual object class object
segmentation datasets and the Weizmann horse dataset. The experimental results
demonstrate that the proposed algorithm achieves a competitive performance when
compared with the state of the arts.
author:
- first_name: Wei
full_name: Xia, Wei
last_name: Xia
- first_name: Csaba
full_name: Domokos, Csaba
id: 492DACF8-F248-11E8-B48F-1D18A9856A87
last_name: Domokos
- first_name: Junjun
full_name: Xiong, Junjun
last_name: Xiong
- first_name: Loongfah
full_name: Cheong, Loongfah
last_name: Cheong
- first_name: Shuicheng
full_name: Yan, Shuicheng
last_name: Yan
citation:
ama: Xia W, Domokos C, Xiong J, Cheong L, Yan S. Segmentation over detection via
optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for
Video Technology. 2015;25(8):1295-1308. doi:10.1109/TCSVT.2014.2379972
apa: Xia, W., Domokos, C., Xiong, J., Cheong, L., & Yan, S. (2015). Segmentation
over detection via optimal sparse reconstructions. IEEE Transactions on Circuits
and Systems for Video Technology. IEEE. https://doi.org/10.1109/TCSVT.2014.2379972
chicago: Xia, Wei, Csaba Domokos, Junjun Xiong, Loongfah Cheong, and Shuicheng Yan.
“Segmentation over Detection via Optimal Sparse Reconstructions.” IEEE Transactions
on Circuits and Systems for Video Technology. IEEE, 2015. https://doi.org/10.1109/TCSVT.2014.2379972.
ieee: W. Xia, C. Domokos, J. Xiong, L. Cheong, and S. Yan, “Segmentation over detection
via optimal sparse reconstructions,” IEEE Transactions on Circuits and Systems
for Video Technology, vol. 25, no. 8. IEEE, pp. 1295–1308, 2015.
ista: Xia W, Domokos C, Xiong J, Cheong L, Yan S. 2015. Segmentation over detection
via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems
for Video Technology. 25(8), 1295–1308.
mla: Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”
IEEE Transactions on Circuits and Systems for Video Technology, vol. 25,
no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.
short: W. Xia, C. Domokos, J. Xiong, L. Cheong, S. Yan, IEEE Transactions on Circuits
and Systems for Video Technology 25 (2015) 1295–1308.
date_created: 2018-12-11T11:52:34Z
date_published: 2015-08-01T00:00:00Z
date_updated: 2021-01-12T06:51:26Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/TCSVT.2014.2379972
intvolume: ' 25'
issue: '8'
language:
- iso: eng
month: '08'
oa_version: None
page: 1295 - 1308
publication: IEEE Transactions on Circuits and Systems for Video Technology
publication_status: published
publisher: IEEE
publist_id: '5638'
quality_controlled: '1'
scopus_import: 1
status: public
title: Segmentation over detection via optimal sparse reconstructions
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2015'
...
---
_id: '1570'
abstract:
- lang: eng
text: Grounding autonomous behavior in the nervous system is a fundamental challenge
for neuroscience. In particular, self-organized behavioral development provides
more questions than answers. Are there special functional units for curiosity,
motivation, and creativity? This paper argues that these features can be grounded
in synaptic plasticity itself, without requiring any higher-level constructs.
We propose differential extrinsic plasticity (DEP) as a new synaptic rule for
self-learning systems and apply it to a number of complex robotic systems as a
test case. Without specifying any purpose or goal, seemingly purposeful and adaptive
rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
These surprising results require no systemspecific modifications of the DEP rule.
They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
is due to the tight brain body environment coupling. The new synaptic rule is
biologically plausible and would be an interesting target for neurobiological
investigation. We also argue that this neuronal mechanism may have been a catalyst
in natural evolution.
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112
apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the
development of sensorimotor intelligence. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1508400112
chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1508400112.
ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy
of Sciences, pp. E6224–E6232, 2015.
ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy
of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.
short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2021-01-12T06:51:40Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
pmid:
- '26504200'
intvolume: ' 112'
issue: '45'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: 1
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '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: '1425'
abstract:
- lang: eng
text: 'In this work we aim at extending the theoretical foundations of lifelong
learning. Previous work analyzing this scenario is based on the assumption that
learning tasks are sampled i.i.d. from a task environment or limited to strongly
constrained data distributions. Instead, we study two scenarios when lifelong
learning is possible, even though the observed tasks do not form an i.i.d. sample:
first, when they are sampled from the same environment, but possibly with dependencies,
and second, when the task environment is allowed to change over time in a consistent
way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct
generalization of the analogous previous result for the i.i.d. case. For the second
scenario we propose to learn an inductive bias in form of a transfer procedure.
We present a generalization bound and show on a toy example how it can be used
to identify a beneficial transfer algorithm.'
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. Lifelong learning with non-i.i.d. tasks. In: Vol 2015.
Neural Information Processing Systems; 2015:1540-1548.'
apa: 'Pentina, A., & Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks
(Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing
Systems, Montreal, Canada: Neural Information Processing Systems.'
chicago: Pentina, Anastasia, and Christoph Lampert. “Lifelong Learning with Non-i.i.d.
Tasks,” 2015:1540–48. Neural Information Processing Systems, 2015.
ieee: 'A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented
at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol.
2015, pp. 1540–1548.'
ista: 'Pentina A, Lampert C. 2015. Lifelong learning with non-i.i.d. tasks. NIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 2015, 1540–1548.'
mla: Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d.
Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.
short: A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015,
pp. 1540–1548.
conference:
end_date: 2015-12-12
location: Montreal, Canada
name: 'NIPS: Neural Information Processing Systems'
start_date: 2015-12-07
date_created: 2018-12-11T11:51:57Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:50:39Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
intvolume: ' 2015'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.nips.cc/paper/6007-lifelong-learning-with-non-iid-tasks
month: '01'
oa: 1
oa_version: None
page: 1540 - 1548
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5781'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lifelong learning with non-i.i.d. tasks
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2015
year: '2015'
...
---
_id: '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: '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'
...