| Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network AH Phan, K Sobolev, K Sozykin, D Ermilov, J Gusak, P Tichavsky, ... European Conference on Computer Vision 2020, 2020 | 126 | 2020 |
| Automated Multi-Stage Compression of Neural Networks J Gusak, M Kholiavchenko, E Ponomarev, L Markeeva, ... Proceedings of the IEEE International Conference on Computer Vision …, 2019 | 69* | 2019 |
| Active subspace of neural networks: Structural analysis and universal attacks C Cui, K Zhang, T Daulbaev, J Gusak, I Oseledets, Z Zhang SIAM Journal on Mathematics of Data Science 2 (4), 1096-1122, 2020 | 34 | 2020 |
| Interpolation technique to speed up gradients propagation in neural odes T Daulbaev, A Katrutsa, L Markeeva, J Gusak, A Cichocki, I Oseledets Advances in Neural Information Processing Systems 33, 16689-16700, 2020 | 27 | 2020 |
| Optimal control and sensitivity analysis for two risk models E Bulinskaya, J Gusak Communications in Statistics-Simulation and Computation 45 (5), 1451-1466, 2016 | 22 | 2016 |
| Discrete-time insurance model with capital injections and reinsurance E Bulinskaya, J Gusak, A Muromskaya Methodology and Computing in Applied Probability 17 (4), 899-914, 2015 | 21 | 2015 |
| Towards Understanding Normalization in Neural ODEs J Gusak, L Markeeva, T Daulbaev, A Katrutsa, A Cichocki, I Oseledets International Conference on Learning Representations 2020 Workshop on …, 2020 | 17 | 2020 |
| Survey on Efficient Training of Large Neural Networks J Gusak, D Cherniuk, A Shilova, A Katrutsa, D Bershatsky, X Zhao, ... IJCAI-ECAI, 2022 | 15* | 2022 |
| Interpolated adjoint method for neural odes T Daulbaev, A Katrutsa, L Markeeva, J Gusak, A Cichocki, I Oseledets arXiv preprint arXiv:2003.05271, 2020 | 14 | 2020 |
| Reduced-order modeling of deep neural networks J Gusak, T Daulbaev, E Ponomarev, A Cichocki, I Oseledets Computational Mathematics and Mathematical Physics 61 (5), 774-785, 2021 | 10 | 2021 |
| Few-bit backward: Quantized gradients of activation functions for memory footprint reduction GS Novikov, D Bershatsky, J Gusak, A Shonenkov, DV Dimitrov, ... International Conference on Machine Learning, 26363-26381, 2023 | 8 | 2023 |
| Automated multi-stage compression of neural networks. 2019 IEEE J Gusak, M Kholyavchenko, E Ponomarev, L Markeeva, ... CVF International Conference on Computer Vision Workshop (ICCVW) pp, 2501-2508, 2019 | 5 | 2019 |
| Insurance Models Under Incomplete Information E Bulinskaya, J Gusak Springer Proceedings in Mathe-matics and Statistics 231, 2018 | 5 | 2018 |
| Memory-efficient backpropagation through large linear layers D Bershatsky, A Mikhalev, A Katrutsa, J Gusak, D Merkulov, I Oseledets arXiv preprint arXiv:2201.13195, 2022 | 4 | 2022 |
| Efficient GPT Model Pre-training using Tensor Train Matrix Representation V Chekalina, G Novikov, J Gusak, I Oseledets, A Panchenko Pacific Asia Conference on Language, Information and Computation, 2023 | 1 | 2023 |
| Meta-solver for neural ordinary differential equations J Gusak, A Katrutsa, T Daulbaev, A Cichocki, I Oseledets arXiv preprint arXiv:2103.08561, 2021 | 1 | 2021 |
| Quantization Aware Factorization for Deep Neural Network Compression D Cherniuk, S Abukhovich, AH Phan, I Oseledets, A Cichocki, J Gusak arXiv preprint arXiv:2308.04595, 2023 | | 2023 |
| Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch X Zhao, T Le Hellard, L Eyraud-Dubois, J Gusak, O Beaumont International Conference on Machine Learning, 2023 | | 2023 |
| H-Rockmate: Hierarchical Approach for Efficient Re-materialization of Large Neural Networks J Gusak, X Zhao, T Le Hellard, Z Li, L Eyraud-Dubois, O Beaumont | | 2023 |
| Stability of solutions in optimal reinsurance problem YV Gusak Moscow University Mathematics Bulletin 72 (2), 73-76, 2017 | | 2017 |
Ivan OseledetsAIRI; Skolkovo Institute of Science and TechnologyVerified email at skoltech.ru
