9 Publications

Mark all

[9]
2019 | Conference Paper | IST-REx-ID: 7468 | OA
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
View | DOI | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 5978 | OA
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.
View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Journal Article | IST-REx-ID: 703 | OA
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
View | DOI | Download Preprint (ext.) | arXiv
 
[6]
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
 
[5]
2017 | Conference Paper | IST-REx-ID: 646 | OA
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
View | DOI | Download Submitted Version (ext.)
 
[4]
2017 | Conference Paper | IST-REx-ID: 915 | OA
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
View | Files available | DOI
 
[3]
2017 | Conference Paper | IST-REx-ID: 916 | OA
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
View | Files available | DOI
 
[2]
2017 | Conference Paper | IST-REx-ID: 917 | OA
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
View | Files available | DOI
 
[1]
2016 | Research Data | IST-REx-ID: 5557 | OA
Swoboda, P. (2016). Synthetic discrete tomography problems. IST Austria. https://doi.org/10.15479/AT:ISTA:46
View | Files available | DOI
 

Search

Filter Publications

9 Publications

Mark all

[9]
2019 | Conference Paper | IST-REx-ID: 7468 | OA
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
View | DOI | Download Preprint (ext.) | arXiv
 
[8]
2018 | Conference Paper | IST-REx-ID: 5978 | OA
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.
View | Download Preprint (ext.) | arXiv
 
[7]
2018 | Journal Article | IST-REx-ID: 703 | OA
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
View | DOI | Download Preprint (ext.) | arXiv
 
[6]
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
 
[5]
2017 | Conference Paper | IST-REx-ID: 646 | OA
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
View | DOI | Download Submitted Version (ext.)
 
[4]
2017 | Conference Paper | IST-REx-ID: 915 | OA
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
View | Files available | DOI
 
[3]
2017 | Conference Paper | IST-REx-ID: 916 | OA
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
View | Files available | DOI
 
[2]
2017 | Conference Paper | IST-REx-ID: 917 | OA
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
View | Files available | DOI
 
[1]
2016 | Research Data | IST-REx-ID: 5557 | OA
Swoboda, P. (2016). Synthetic discrete tomography problems. IST Austria. https://doi.org/10.15479/AT:ISTA:46
View | Files available | DOI
 

Search

Filter Publications