Paul Swoboda
Kolmogorov Group
9 Publications
2019 | Conference Paper | IST-REx-ID: 7468 | 
Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140
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2018 | Journal Article | IST-REx-ID: 703 |
Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884
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2018 | Conference Paper | IST-REx-ID: 5978 |
Haller, S., Swoboda, P., & Savchynskyy, B. (2018). Exact MAP-inference by confining combinatorial search with LP relaxation. In Proceedings of the 32st AAAI Conference on Artificial Intelligence (pp. 6581–6588). New Orleans, LU, United States: AAAI Press.
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2017 | Conference Paper | IST-REx-ID: 641
Trajkovska, V., Swoboda, P., Åström, F., & Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_26
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2017 | Conference Paper | IST-REx-ID: 646 |
Kuske, J., Swoboda, P., & Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_19
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2017 | Conference Paper | IST-REx-ID: 916 |
Swoboda, P., Rother, C., Abu Alhaija, C., Kainmueller, D., & Savchynskyy, B. (2017). A study of lagrangean decompositions and dual ascent solvers for graph matching (Vol. 2017, pp. 7062–7071). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.747
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2017 | Conference Paper | IST-REx-ID: 915 |
Swoboda, P., & Andres, B. (2017). A message passing algorithm for the minimum cost multicut problem (Vol. 2017, pp. 4990–4999). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.530
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2017 | Conference Paper | IST-REx-ID: 917 |
Swoboda, P., Kuske, J., & Savchynskyy, B. (2017). A dual ascent framework for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.526
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2016 | Research Data | IST-REx-ID: 5557 |
Swoboda, P. (2016). Synthetic discrete tomography problems. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:46
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9 Publications
2019 | Conference Paper | IST-REx-ID: 7468 |
Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2018 | Journal Article | IST-REx-ID: 703 |
Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5978 |
Haller, S., Swoboda, P., & Savchynskyy, B. (2018). Exact MAP-inference by confining combinatorial search with LP relaxation. In Proceedings of the 32st AAAI Conference on Artificial Intelligence (pp. 6581–6588). New Orleans, LU, United States: AAAI Press.
[Preprint]
View
| Download Preprint (ext.)
| WoS
| arXiv
2017 | Conference Paper | IST-REx-ID: 641
Trajkovska, V., Swoboda, P., Åström, F., & Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_26
View
| DOI
2017 | Conference Paper | IST-REx-ID: 646 |
Kuske, J., Swoboda, P., & Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_19
[Submitted Version]
View
| DOI
| Download Submitted Version (ext.)
2017 | Conference Paper | IST-REx-ID: 916 |
Swoboda, P., Rother, C., Abu Alhaija, C., Kainmueller, D., & Savchynskyy, B. (2017). A study of lagrangean decompositions and dual ascent solvers for graph matching (Vol. 2017, pp. 7062–7071). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.747
[Submitted Version]
View
| Files available
| DOI
| WoS
2017 | Conference Paper | IST-REx-ID: 915 |
Swoboda, P., & Andres, B. (2017). A message passing algorithm for the minimum cost multicut problem (Vol. 2017, pp. 4990–4999). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.530
[Submitted Version]
View
| Files available
| DOI
| WoS
2017 | Conference Paper | IST-REx-ID: 917 |
Swoboda, P., Kuske, J., & Savchynskyy, B. (2017). A dual ascent framework for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.526
[Submitted Version]
View
| Files available
| DOI
| WoS
2016 | Research Data | IST-REx-ID: 5557 |
Swoboda, P. (2016). Synthetic discrete tomography problems. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:46
[Published Version]
View
| Files available
| DOI