---
_id: '1794'
abstract:
- lang: eng
text: We consider Conditional random fields (CRFs) with pattern-based potentials
defined on a chain. In this model the energy of a string (labeling) (Formula presented.)
is the sum of terms over intervals [i, j] where each term is non-zero only if
the substring (Formula presented.) equals a prespecified pattern w. Such CRFs
can be naturally applied to many sequence tagging problems. We present efficient
algorithms for the three standard inference tasks in a CRF, namely computing (i)
the partition function, (ii) marginals, and (iii) computing the MAP. Their complexities
are respectively (Formula presented.), (Formula presented.) and (Formula presented.)
where L is the combined length of input patterns, (Formula presented.) is the
maximum length of a pattern, and D is the input alphabet. This improves on the
previous algorithms of Ye et al. (NIPS, 2009) whose complexities are respectively
(Formula presented.), (Formula presented.) and (Formula presented.), where (Formula
presented.) is the number of input patterns. In addition, we give an efficient
algorithm for sampling, and revisit the case of MAP with non-positive weights.
acknowledgement: This work has been partially supported by the European Research Council
under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant
agreement no. 616160.
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Rustem
full_name: Takhanov, Rustem
id: 2CCAC26C-F248-11E8-B48F-1D18A9856A87
last_name: Takhanov
citation:
ama: Kolmogorov V, Takhanov R. Inference algorithms for pattern-based CRFs on sequence
data. Algorithmica. 2016;76(1):17-46. doi:10.1007/s00453-015-0017-7
apa: Kolmogorov, V., & Takhanov, R. (2016). Inference algorithms for pattern-based
CRFs on sequence data. Algorithmica. Springer. https://doi.org/10.1007/s00453-015-0017-7
chicago: Kolmogorov, Vladimir, and Rustem Takhanov. “Inference Algorithms for Pattern-Based
CRFs on Sequence Data.” Algorithmica. Springer, 2016. https://doi.org/10.1007/s00453-015-0017-7.
ieee: V. Kolmogorov and R. Takhanov, “Inference algorithms for pattern-based CRFs
on sequence data,” Algorithmica, vol. 76, no. 1. Springer, pp. 17–46, 2016.
ista: Kolmogorov V, Takhanov R. 2016. Inference algorithms for pattern-based CRFs
on sequence data. Algorithmica. 76(1), 17–46.
mla: Kolmogorov, Vladimir, and Rustem Takhanov. “Inference Algorithms for Pattern-Based
CRFs on Sequence Data.” Algorithmica, vol. 76, no. 1, Springer, 2016, pp.
17–46, doi:10.1007/s00453-015-0017-7.
short: V. Kolmogorov, R. Takhanov, Algorithmica 76 (2016) 17–46.
date_created: 2018-12-11T11:54:02Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2023-10-17T09:51:31Z
day: '01'
department:
- _id: VlKo
doi: 10.1007/s00453-015-0017-7
ec_funded: 1
external_id:
arxiv:
- '1210.0508'
intvolume: ' 76'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1210.0508
month: '09'
oa: 1
oa_version: Preprint
page: 17 - 46
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Algorithmica
publication_status: published
publisher: Springer
publist_id: '5316'
quality_controlled: '1'
related_material:
record:
- id: '2272'
relation: earlier_version
status: public
scopus_import: 1
status: public
title: Inference algorithms for pattern-based CRFs on sequence data
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 76
year: '2016'
...
---
_id: '5557'
abstract:
- lang: eng
text: "Small synthetic discrete tomography problems.\r\nSizes are 32x32, 64z64 and
256x256.\r\nProjection angles are 2, 4, and 6.\r\nNumber of labels are 3 and 5."
article_processing_charge: No
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
citation:
ama: Swoboda P. Synthetic discrete tomography problems. 2016. doi:10.15479/AT:ISTA:46
apa: Swoboda, P. (2016). Synthetic discrete tomography problems. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:46
chicago: Swoboda, Paul. “Synthetic Discrete Tomography Problems.” Institute of Science
and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:46.
ieee: P. Swoboda, “Synthetic discrete tomography problems.” Institute of Science
and Technology Austria, 2016.
ista: Swoboda P. 2016. Synthetic discrete tomography problems, Institute of Science
and Technology Austria, 10.15479/AT:ISTA:46.
mla: Swoboda, Paul. Synthetic Discrete Tomography Problems. Institute of
Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:46.
short: P. Swoboda, (2016).
contributor:
- contributor_type: data_collector
first_name: Jan
last_name: Kuske
datarep_id: '46'
date_created: 2018-12-12T12:31:31Z
date_published: 2016-09-20T00:00:00Z
date_updated: 2024-02-21T13:50:21Z
day: '20'
ddc:
- '006'
department:
- _id: VlKo
doi: 10.15479/AT:ISTA:46
file:
- access_level: open_access
checksum: aa5a16a0dc888da7186fb8fc45e88439
content_type: application/zip
creator: system
date_created: 2018-12-12T13:05:19Z
date_updated: 2020-07-14T12:47:02Z
file_id: '5645'
file_name: IST-2016-46-v1+1_discrete_tomography_synthetic.zip
file_size: 36058401
relation: main_file
file_date_updated: 2020-07-14T12:47:02Z
has_accepted_license: '1'
keyword:
- discrete tomography
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '09'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
status: public
title: Synthetic discrete tomography problems
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2016'
...
---
_id: '1636'
abstract:
- lang: eng
text: "Constraint Satisfaction Problem (CSP) is a fundamental algorithmic problem
that appears in many areas of Computer Science. It can be equivalently stated
as computing a homomorphism R→ΓΓ between two relational structures, e.g. between
two directed graphs. Analyzing its complexity has been a prominent research direction,
especially for the fixed template CSPs where the right side ΓΓ is fixed and the
left side R is unconstrained.\r\n\r\nFar fewer results are known for the hybrid
setting that restricts both sides simultaneously. It assumes that R belongs to
a certain class of relational structures (called a structural restriction in this
paper). We study which structural restrictions are effective, i.e. there exists
a fixed template ΓΓ (from a certain class of languages) for which the problem
is tractable when R is restricted, and NP-hard otherwise. We provide a characterization
for structural restrictions that are closed under inverse homomorphisms. The criterion
is based on the chromatic number of a relational structure defined in this paper;
it generalizes the standard chromatic number of a graph.\r\n\r\nAs our main tool,
we use the algebraic machinery developed for fixed template CSPs. To apply it
to our case, we introduce a new construction called a “lifted language”. We also
give a characterization for structural restrictions corresponding to minor-closed
families of graphs, extend results to certain Valued CSPs (namely conservative
valued languages), and state implications for (valued) CSPs with ordered variables
and for the maximum weight independent set problem on some restricted families
of graphs."
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Michal
full_name: Rolinek, Michal
id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87
last_name: Rolinek
- first_name: Rustem
full_name: Takhanov, Rustem
last_name: Takhanov
citation:
ama: 'Kolmogorov V, Rolinek M, Takhanov R. Effectiveness of structural restrictions
for hybrid CSPs. In: 26th International Symposium. Vol 9472. Springer Nature;
2015:566-577. doi:10.1007/978-3-662-48971-0_48'
apa: 'Kolmogorov, V., Rolinek, M., & Takhanov, R. (2015). Effectiveness of structural
restrictions for hybrid CSPs. In 26th International Symposium (Vol. 9472,
pp. 566–577). Nagoya, Japan: Springer Nature. https://doi.org/10.1007/978-3-662-48971-0_48'
chicago: Kolmogorov, Vladimir, Michal Rolinek, and Rustem Takhanov. “Effectiveness
of Structural Restrictions for Hybrid CSPs.” In 26th International Symposium,
9472:566–77. Springer Nature, 2015. https://doi.org/10.1007/978-3-662-48971-0_48.
ieee: V. Kolmogorov, M. Rolinek, and R. Takhanov, “Effectiveness of structural restrictions
for hybrid CSPs,” in 26th International Symposium, Nagoya, Japan, 2015,
vol. 9472, pp. 566–577.
ista: 'Kolmogorov V, Rolinek M, Takhanov R. 2015. Effectiveness of structural restrictions
for hybrid CSPs. 26th International Symposium. ISAAC: International Symposium
on Algorithms and Computation, LNCS, vol. 9472, 566–577.'
mla: Kolmogorov, Vladimir, et al. “Effectiveness of Structural Restrictions for
Hybrid CSPs.” 26th International Symposium, vol. 9472, Springer Nature,
2015, pp. 566–77, doi:10.1007/978-3-662-48971-0_48.
short: V. Kolmogorov, M. Rolinek, R. Takhanov, in:, 26th International Symposium,
Springer Nature, 2015, pp. 566–577.
conference:
end_date: 2015-12-11
location: Nagoya, Japan
name: 'ISAAC: International Symposium on Algorithms and Computation'
start_date: 2015-12-09
date_created: 2018-12-11T11:53:10Z
date_published: 2015-12-01T00:00:00Z
date_updated: 2022-02-01T15:12:35Z
day: '01'
department:
- _id: VlKo
doi: 10.1007/978-3-662-48971-0_48
ec_funded: 1
external_id:
arxiv:
- '1504.07067'
intvolume: ' 9472'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1504.07067
month: '12'
oa: 1
oa_version: Preprint
page: 566 - 577
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: 26th International Symposium
publication_identifier:
isbn:
- 978-3-662-48970-3
publication_status: published
publisher: Springer Nature
publist_id: '5519'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Effectiveness of structural restrictions for hybrid CSPs
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 9472
year: '2015'
...
---
_id: '1841'
abstract:
- lang: eng
text: We propose a new family of message passing techniques for MAP estimation in
graphical models which we call Sequential Reweighted Message Passing (SRMP). Special
cases include well-known techniques such as Min-Sum Diffusion (MSD) and a faster
Sequential Tree-Reweighted Message Passing (TRW-S). Importantly, our derivation
is simpler than the original derivation of TRW-S, and does not involve a decomposition
into trees. This allows easy generalizations. The new family of algorithms can
be viewed as a generalization of TRW-S from pairwise to higher-order graphical
models. We test SRMP on several real-world problems with promising results.
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: Kolmogorov V. A new look at reweighted message passing. IEEE Transactions
on Pattern Analysis and Machine Intelligence. 2015;37(5):919-930. doi:10.1109/TPAMI.2014.2363465
apa: Kolmogorov, V. (2015). A new look at reweighted message passing. IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2014.2363465
chicago: Kolmogorov, Vladimir. “A New Look at Reweighted Message Passing.” IEEE
Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2015. https://doi.org/10.1109/TPAMI.2014.2363465.
ieee: V. Kolmogorov, “A new look at reweighted message passing,” IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 37, no. 5. IEEE, pp. 919–930,
2015.
ista: Kolmogorov V. 2015. A new look at reweighted message passing. IEEE Transactions
on Pattern Analysis and Machine Intelligence. 37(5), 919–930.
mla: Kolmogorov, Vladimir. “A New Look at Reweighted Message Passing.” IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, IEEE, 2015,
pp. 919–30, doi:10.1109/TPAMI.2014.2363465.
short: V. Kolmogorov, IEEE Transactions on Pattern Analysis and Machine Intelligence
37 (2015) 919–930.
date_created: 2018-12-11T11:54:18Z
date_published: 2015-05-01T00:00:00Z
date_updated: 2021-01-12T06:53:33Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/TPAMI.2014.2363465
ec_funded: 1
intvolume: ' 37'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1309.5655
month: '05'
oa: 1
oa_version: Preprint
page: 919 - 930
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '5261'
quality_controlled: '1'
scopus_import: 1
status: public
title: A new look at reweighted message passing
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
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'
...