| Tensorizing neural networks A Novikov, D Podoprikhin, A Osokin, DP Vetrov NIPS, Advances in Neural Information Processing Systems, 442-450, 2015 | 839 | 2015 |
| Fast approximate energy minimization with label costs A Delong, A Osokin, HN Isack, Y Boykov IJCV, International journal of computer vision 96 (1), 1-27, 2012 | 669 | 2012 |
| Fast approximate energy minimization with label costs A Delong, A Osokin, HN Isack, Y Boykov CVPR, Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2010 | 669 | 2010 |
| Breaking Sticks and Ambiguities with Adaptive Skip-gram S Bartunov, D Kondrashkin, A Osokin, D Vetrov AISTATS, Proceedings of the International Conference on Artificial …, 2016 | 201 | 2016 |
| Context-aware CNNs for person head detection TH Vu, A Osokin, I Laptev ICCV, Proceedings of the International Conference on Computer Vision, 2893-2901, 2015 | 148 | 2015 |
| GANs for Biological Image Synthesis A Osokin, A Chessel, REC Salas, F Vaggi ICCV, Proceedings of the International Conference on Computer Vision, 2017 | 116 | 2017 |
| A principled deep random field model for image segmentation P Kohli, A Osokin, S Jegelka CVPR, Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 89 | 2013 |
| Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs A Osokin, JB Alayrac, I Lukasewitz, PK Dokania, S Lacoste-Julien ICML, Proceedings of the International Conference on Machine Learning, 2016 | 82 | 2016 |
| Putting MRFs on a Tensor Train. A Novikov, A Rodomanov, A Osokin, D Vetrov ICML, Proceedings of the International Conference on Machine Learning, 811-819, 2014 | 50 | 2014 |
| On structured prediction theory with calibrated convex surrogate losses A Osokin, F Bach, S Lacoste-Julien NIPS, Advances in Neural Information Processing Systems, 301-312, 2017 | 49 | 2017 |
| SEARNN: Training RNNs with Global-Local Losses R Leblond, JB Alayrac, A Osokin, S Lacoste-Julien ICLR, Proceedings of International Conference on Learning Representations, 2018 | 42 | 2018 |
| OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features A Osokin, D Sumin, V Lomakin ECCV, Proceedings of European Conference on Computer Vision, 2020 | 35 | 2020 |
| Submodular decomposition framework for inference in associative markov networks with global constraints A Osokin, D Vetrov, V Kolmogorov CVPR, Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2011 | 24 | 2011 |
| Minimizing sparse high-order energies by submodular vertex-cover A Delong, O Veksler, A Osokin, Y Boykov NIPS, Advances in Neural Information Processing Systems, 962-970, 2012 | 15 | 2012 |
| Perceptually inspired layout-aware losses for image segmentation A Osokin, P Kohli ECCV, Proceedings of the European Conference on Computer Vision, 663-678, 2014 | 11 | 2014 |
| SPARQLing Database Queries from Intermediate Question Decompositions I Saparina, A Osokin EMNLP, the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 | 7 | 2021 |
| Submodular relaxation for inference in Markov random fields A Osokin, DP Vetrov TPAMI, IEEE transactions on pattern analysis and machine intelligence 37 (7 …, 2015 | 7 | 2015 |
| 3-D mouse brain model reconstruction from a sequence of 2-D slices in application to Allen brain atlas A Osokin, D Vetrov, D Kropotov International Meeting on Computational Intelligence Methods for …, 2010 | 7 | 2010 |
| Submodular relaxation for MRFs with high-order potentials A Osokin, D Vetrov ECCV workshops, Proceedings of the workshops of European Conference on …, 2012 | 6 | 2012 |
| Variational segmentation algorithms with label frequency constraints D Kropotov, D Laptev, A Osokin, D Vetrov Pattern Recognition and Image Analysis 20 (3), 324-334, 2010 | 6 | 2010 |
Dmitry VetrovHigher School of Economics, AI Research Institute, MoscowVerified email at hse.ru
