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ORCIDAnton Osokin
Person information
- affiliation: National Research University Higher School of Economics, Moscow, Russia
- affiliation (2014 - 2017): INRIA, France
- affiliation (2014 - 2017): École Normale Supérieure, Paris, France
- affiliation (PhD 2014): Lomonosov Moscow State University, Russia
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2020 – today
- 2023
[c26]Anton Osokin, Irina Saparina, Ramil Yarullin:
Searching for Better Database Queries in the Outputs of Semantic Parsers. EACL (Findings) 2023: 2198-2211- 2022
- [i19]Anton Osokin, Irina Saparina, Ramil Yarullin:
Searching for Better Database Queries in the Outputs of Semantic Parsers. CoRR abs/2210.07201 (2022) - 2021
- [c25]Irina Saparina, Anton Osokin:
SPARQLing Database Queries from Intermediate Question Decompositions. EMNLP (1) 2021: 8984-8998 - [i18]Irina Saparina, Anton Osokin:
SPARQLing Database Queries from Intermediate Question Decompositions. CoRR abs/2109.06162 (2021) - 2020
- [c24]Anton Osokin
, Denis Sumin, Vasily Lomakin:
OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features. ECCV (15) 2020: 635-652 - [i17]Anton Osokin, Denis Sumin, Vasily Lomakin:
OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features. CoRR abs/2003.06800 (2020)
2010 – 2019
- 2019
- [c23]Anton Osokin:
How to Put Algorithms into Neural Networks. DAMDID/RCDL 2019: 1-2 - [c22]Anton Osokin:
Three Simple Approaches to Combining Neural Networks with Algorithms. DAMDID/RCDL (Selected Papers) 2019: 3-12 - [c21]Nikolay Kondratyuk, Grigory S. Smirnov, Alexander Agarkov, Anton Osokin, Vsevolod P. Nikolskiy, Alexander Semenov, Vladimir V. Stegailov:
Performance and Scalability of Materials Science and Machine Learning Codes on the State-of-Art Hybrid Supercomputer Architecture. RuSCDays 2019: 597-609 - [i16]Aleksandr Shevchenko, Anton Osokin:
Scaling Matters in Deep Structured-Prediction Models. CoRR abs/1902.11088 (2019) - [i15]Irina Saparina, Anton Osokin:
Cost-Sensitive Training for Autoregressive Models. CoRR abs/1912.03771 (2019) - 2018
- [c20]Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien:
SEARNN: Training RNNs with global-local losses. ICLR (Poster) 2018 - [c19]Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin:
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. NeurIPS 2018: 667-675 - [c18]Tatiana Shpakova, Francis R. Bach, Anton Osokin:
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models. UAI 2018: 279-289 - [i14]Guilhem Chéron, Anton Osokin, Ivan Laptev, Cordelia Schmid:
Modeling Spatio-Temporal Human Track Structure for Action Localization. CoRR abs/1806.11008 (2018) - [i13]Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin:
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. CoRR abs/1810.11544 (2018) - [i12]Tatiana Shpakova, Francis R. Bach, Anton Osokin:
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models. CoRR abs/1811.08725 (2018) - [i11]Tuan-Hung Vu, Anton Osokin, Ivan Laptev:
Tube-CNN: Modeling temporal evolution of appearance for object detection in video. CoRR abs/1812.02619 (2018) - 2017
- [c17]Anton Osokin, Anatole Chessel, Rafael Edgardo Carazo-Salas, Federico Vaggi:
GANs for Biological Image Synthesis. ICCV 2017: 2252-2261 - [c16]Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. NIPS 2017: 302-313 - [i10]Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. CoRR abs/1703.02403 (2017) - [i9]Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien:
SEARNN: Training RNNs with Global-Local Losses. CoRR abs/1706.04499 (2017) - [i8]Anton Osokin, Anatole Chessel, Rafael Edgardo Carazo-Salas, Federico Vaggi:
GANs for Biological Image Synthesis. CoRR abs/1708.04692 (2017) - 2016
- [c15]Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov:
Breaking Sticks and Ambiguities with Adaptive Skip-gram. AISTATS 2016: 130-138 - [c14]Alexander Kirillov, Mikhail Gavrikov, Ekaterina Lobacheva, Anton Osokin, Dmitry P. Vetrov:
Deep Part-Based Generative Shape Model with Latent Variables. BMVC 2016 - [c13]Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Kumar Dokania, Simon Lacoste-Julien:
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs. ICML 2016: 593-602 - [i7]Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Kumar Dokania, Simon Lacoste-Julien:
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs. CoRR abs/1605.09346 (2016) - 2015
- [j2]Anton Osokin, Dmitry P. Vetrov:
Submodular Relaxation for Inference in Markov Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1347-1359 (2015) - [c12]Tuan-Hung Vu, Anton Osokin, Ivan Laptev:
Context-Aware CNNs for Person Head Detection. ICCV 2015: 2893-2901 - [c11]Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov:
Tensorizing Neural Networks. NIPS 2015: 442-450 - [i6]Anton Osokin, Dmitry P. Vetrov:
Submodular relaxation for inference in Markov random fields. CoRR abs/1501.03771 (2015) - [i5]Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry P. Vetrov:
Breaking Sticks and Ambiguities with Adaptive Skip-gram. CoRR abs/1502.07257 (2015) - [i4]Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry P. Vetrov:
Tensorizing Neural Networks. CoRR abs/1509.06569 (2015) - [i3]Tuan-Hung Vu, Anton Osokin, Ivan Laptev:
Context-aware CNNs for person head detection. CoRR abs/1511.07917 (2015) - 2014
- [c10]Anton Osokin, Pushmeet Kohli:
Perceptually Inspired Layout-Aware Losses for Image Segmentation. ECCV (2) 2014: 663-678 - [c9]Roman Shapovalov, Dmitry P. Vetrov, Anton Osokin, Pushmeet Kohli:
Multi-utility Learning: Structured-Output Learning with Multiple Annotation-Specific Loss Functions. EMMCVPR 2014: 406-420 - [c8]Alexander Novikov, Anton Rodomanov, Anton Osokin, Dmitry P. Vetrov:
Putting MRFs on a Tensor Train. ICML 2014: 811-819 - [i2]Roman Shapovalov, Dmitry P. Vetrov, Anton Osokin, Pushmeet Kohli:
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions. CoRR abs/1406.5910 (2014) - 2013
- [c7]Pushmeet Kohli, Anton Osokin, Stefanie Jegelka:
A Principled Deep Random Field Model for Image Segmentation. CVPR 2013: 1971-1978 - 2012
- [j1]Andrew Delong, Anton Osokin, Hossam N. Isack, Yuri Boykov:
Fast Approximate Energy Minimization with Label Costs. Int. J. Comput. Vis. 96(1): 1-27 (2012) - [c6]Anton Osokin, Dmitry P. Vetrov:
Submodular Relaxation for MRFs with High-Order Potentials. ECCV Workshops (3) 2012: 305-314 - [c5]Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov:
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover. NIPS 2012: 971-979 - 2011
- [c4]Anton Osokin, Dmitry P. Vetrov, Vladimir Kolmogorov:
Submodular decomposition framework for inference in associative Markov networks with global constraints. CVPR 2011: 1889-1896 - [i1]Anton Osokin, Dmitry P. Vetrov, Vladimir Kolmogorov:
Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints. CoRR abs/1103.1077 (2011) - 2010
- [c3]Anton Osokin, Dmitry P. Vetrov, Alexey Lebedev, Vladimir Galatenko, Dmitry Kropotov, Konstantin V. Anokhin:
An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice. CIBB 2010: 86-97 - [c2]Andrew Delong, Anton Osokin, Hossam N. Isack, Yuri Boykov:
Fast approximate energy minimization with label costs. CVPR 2010: 2173-2180
2000 – 2009
- 2009
- [c1]Anton Osokin, Dmitry P. Vetrov, Dmitry Kropotov:
3-D Mouse Brain Model Reconstruction from a Sequence of 2-D Slices in Application to Allen Brain Atlas. CIBB 2009: 291-303
Coauthor Index
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