Paul Swoboda
Kolmogorov Group
9 Publications
2019 | Conference Paper | IST-REx-ID: 7468 | 
P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol. 2019–June.
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| arXiv
2018 | Journal Article | IST-REx-ID: 703 |
A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 5978 |
S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588.
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| arXiv
2017 | Conference Paper | IST-REx-ID: 641
V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.
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| DOI
2017 | Conference Paper | IST-REx-ID: 646 |
J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.
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2017 | Conference Paper | IST-REx-ID: 916 |
P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.
[Submitted Version]
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2017 | Conference Paper | IST-REx-ID: 915 |
P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.
[Submitted Version]
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| WoS
2017 | Conference Paper | IST-REx-ID: 917 |
P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.
[Submitted Version]
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| WoS
2016 | Research Data | IST-REx-ID: 5557 |
P. Swoboda, “Synthetic discrete tomography problems.” Institute of Science and Technology Austria, 2016.
[Published Version]
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| Files available
| DOI
9 Publications
2019 | Conference Paper | IST-REx-ID: 7468 |
P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol. 2019–June.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2018 | Journal Article | IST-REx-ID: 703 |
A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5978 |
S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588.
[Preprint]
View
| Download Preprint (ext.)
| WoS
| arXiv
2017 | Conference Paper | IST-REx-ID: 641
V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.
View
| DOI
2017 | Conference Paper | IST-REx-ID: 646 |
J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.
[Submitted Version]
View
| DOI
| Download Submitted Version (ext.)
2017 | Conference Paper | IST-REx-ID: 916 |
P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.
[Submitted Version]
View
| Files available
| DOI
| WoS
2017 | Conference Paper | IST-REx-ID: 915 |
P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.
[Submitted Version]
View
| Files available
| DOI
| WoS
2017 | Conference Paper | IST-REx-ID: 917 |
P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.
[Submitted Version]
View
| Files available
| DOI
| WoS
2016 | Research Data | IST-REx-ID: 5557 |
P. Swoboda, “Synthetic discrete tomography problems.” Institute of Science and Technology Austria, 2016.
[Published Version]
View
| Files available
| DOI