Anton Osokin
- Associate Professor:Faculty of Computer Science / Big Data and Information Retrieval School
- Leading Research Fellow:Faculty of Computer Science / Big Data and Information Retrieval School / Yandex Laboratory
- Anton Osokin has been at HSE University since 2017.
Education and Degrees
- 2014
Candidate of Sciences* (PhD) in Discrete Mathematics and Mathematical Cybernetics
Lomonosov Moscow State University
Thesis Title: Submodular relaxation for energy minimization in Markov random fields - 2010
Degree in Applied Mathematics and Computer Science
Lomonosov Moscow State University, Computational Mathematics and Cybernetics
* Candidate of Sciences
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Employment history
École Normale Supérieure & INRIA, Paris, France
Computer Science Department
Postdoctoral researcher in WILLOW project-team
October 2016 – August 2017
École Normale Supérieure & INRIA, Paris, France
Computer Science Department
Postdoctoral researcher in SIERRA project-team
October 2014 – September 2016
Moscow State University, Moscow, Russia
Faculty of Computational Mathematics and Cybernetics
Assistant in the department of mathematical methods of forecasting
October 2012 –September 2014
20201
Chapter Osokin A., Sumin D., Lomakin V. OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features, in: Computer Vision – ECCV 2020; 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV Vol. 12360. NY : Springer, 2020. doi P. 635-652. doi
20183
- Chapter Shpakova T., Bach F., Osokin A. Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models, in: Proceedings of the international conference on Uncertainty in Artificial Intelligence (UAI 2018). , 2018. P. 1-11.
- Chapter Struminsky K., Lacoste-Julien S., Osokin A. Quantifying Learning Guarantees for Convex but Inconsistent Surrogates, in: Advances in Neural Information Processing Systems 31 (NIPS 2018). , 2018. P. 1-9.
- Chapter Leblond R., Alayrac J., Osokin A., Lacoste-Julien S. SEARNN: Training RNNs with global-local losses, in: Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). , 2018. P. 1-16.
20172
- Chapter Osokin A., Chessel A., Carazo Salas R. E., Vaggi F. GANs for Biological Image Synthesis, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017). Venice : IEEE, 2017. P. 2252-2261. doi
- Chapter Osokin A., Bach F., Lacoste-Julien S. On Structured Prediction Theory with Calibrated Convex Surrogate Losses, in: Advances in Neural Information Processing Systems 30 (NIPS 2017). Montreal : Curran Associates, 2017. P. 302-313.
20162
- Chapter Bartunov S., Kondrashkin D., Osokin A., Vetrov D. Breaking Sticks and Ambiguities with Adaptive Skip-gram, in: Proceedings of Machine Learning Research. Proceedings of The International Conference on Artificial Intelligence and Statistics (AISTATS 2016) Vol. 51. Cadiz : , 2016. P. 130-138.
- Chapter Osokin A., Alayrac J., Lukasewitz I., Dokania P., Lacoste-Julien S. Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs, in: Proceedings of Machine Learning Research. Proceedings of the International Conference on Machine Learning (ICML 2016) Vol. 48. NY : , 2016. P. 885-925.
20153
- Chapter Vu T., Osokin A., Laptev I. Context-Aware CNNs for Person Head Detection, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015). Santiago de Chile : IEEE, 2015. P. 2893-2901. doi
- Article Osokin A., Vetrov D. Submodular Relaxation for Inference in Markov Random Fields // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. Vol. 37. No. 7. P. 1347-1359.
- Chapter Novikov A., Podoprikhin D., Osokin A., Vetrov D. Tensorizing Neural Networks, in: Advances in Neural Information Processing Systems 28 (NIPS 2015). NY : Curran Associates, 2015.
20142
- Chapter Osokin A., Kohli P. Perceptually Inspired Layout-Aware Losses for Image Segmentation, in: Lecture Notes in Computer Science. Proceedings of the 13th European Conference on Computer Vision (ECCV 2014) Vol. 8690. Part 2. Zürich : Springer, 2014. P. 663-678. doi
- Chapter Vetrov D., Osokin A., Novikov A., Rodomanov A. Putting MRFs on a Tensor Train, in: JMLR Workshop and Conference Proceedings Issue 32: Proceedings of The 31st International Conference on Machine Learning. Beijing : Microtome Publishing, 2014. P. 811-819.
20131
Chapter Kohli P., Osokin A., Jegelka S. A Principled Deep Random Field Model for Image Segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). Portland : IEEE, 2013. P. 1971-1978. doi
20122
- Article Delong A., Osokin A., Isack H. N., Boykov Y. Fast Approximate Energy Minimization with Label Costs // International Journal of Computer Vision. 2012. Vol. 96. No. 1. P. 1-27. doi
- Chapter Delong A., Veksler O., Osokin A., Boykov Y. Minimizing sparse high-order energies by submodular vertex-cover, in: Advances in Neural Information Processing Systems 26 (NIPS 2012). Lake Tahoe : Curran Associates, Inc., 2012.
20111
Chapter Osokin A., Vetrov D., Kolmogorov V. Submodular decomposition framework for inference in associative Markov networks with global constraints, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011). Colorado Springs : IEEE, 2011. P. 1889-1896. doi
20101
Chapter Delong A., Osokin A., Isack H. N., Boykov Y. Fast approximate energy minimization with label costs, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010). San Francisco : IEEE, 2010. P. 2173-2180. doi
Student Term / Thesis Papers
- Bachelor
E. Glazkova, Semantic Image Segmentation With Deep Structured Models. Faculty of Computer Science, 2019
D. Sharafian, Human Pose Estimation With Deep Structured Models. Faculty of Computer Science, 2019
A. Shevchenko, End-to-End Training of Deep Structured Models. Faculty of Computer Science, 2018
V. Skripniuk, Conditional Generative Adversarial Networks for Biological Image Synthesis. Faculty of Computer Science, 2018
- Master
I. Saparina, Cost-sensitive Training for Autoregressive Models. Faculty of Computer Science, 2020
Courses (2020/2021)
- Deep Learning (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Rus
- Past Courses
Courses (2018/2019)
Modern Methods of Data Analysis: Deep Learning (Master’s programme; Faculty of Computer Science; 1 year, 3, 4 module)Rus
