| Tensorizing neural networks A Novikov, D Podoprikhin, A Osokin, D Vetrov Advances in Neural Information Processing Systems, 442-450, 2015 | 553 | 2015 |
| Ultimate tensorization: compressing convolutional and FC layers alike T Garipov, D Podoprikhin, A Novikov, D Vetrov arXiv preprint arXiv:1611.03214, 2016 | 102 | 2016 |
| Expressive power of recurrent neural networks V Khrulkov, A Novikov, I Oseledets International Conference on Learning Representations (ICLR), 2018 | 77 | 2018 |
| Exponential machines A Novikov, M Trofimov, I Oseledets Bulletin of the Polish Academy of Sciences: Technical Sciences 6, 2018 | 77 | 2018 |
| Acme: A Research Framework for Distributed Reinforcement Learning M Hoffman, B Shahriari, J Aslanides, G Barth-Maron, F Behbahani, ... arXiv preprint arXiv:2006.00979, 2020 | 39 | 2020 |
| Putting MRFs on a tensor train A Novikov, A Rodomanov, A Osokin, D Vetrov International Conference on Machine Learning, 811-819, 2014 | 30 | 2014 |
| Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition P Izmailov, A Novikov, D Kropotov The 21st International Conference on Artificial Intelligence and Statistics …, 2018 | 27 | 2018 |
| Tensor Train decomposition on TensorFlow (T3F) A Novikov, P Izmailov, V Khrulkov, M Figurnov, I Oseledets Journal of Machine Learning Research, Machine Learning Open Source Software …, 2020 | 23 | 2020 |
| Scaling data-driven robotics with reward sketching and batch reinforcement learning S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ... Robotics: Science and Systems (RSS), 2020 | 23* | 2020 |
| Critic Regularized Regression Z Wang, A Novikov, K Żołna, JT Springenberg, S Reed, B Shahriari, ... Conference on Neural Information Processing Systems (NeurIPS), 2020 | 21 | 2020 |
| Hyperparameter Selection for Offline Reinforcement Learning TL Paine, C Paduraru, A Michi, C Gulcehre, K Zolna, A Novikov, Z Wang, ... arXiv preprint arXiv:2007.09055, 2020 | 16 | 2020 |
| RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning C Gulcehre, Z Wang, A Novikov, T Paine, S Gómez, K Zolna, R Agarwal, ... Advances in Neural Information Processing Systems 33, 2020 | 16* | 2020 |
| Task-Relevant Adversarial Imitation Learning K Zolna, S Reed, A Novikov, SG Colmenarej, D Budden, S Cabi, M Denil, ... Conference on Robot Learning (CORL), 2020 | 13 | 2020 |
| Low-rank Riemannian eigensolver for high-dimensional Hamiltonians M Rakhuba, A Novikov, I Oseledets Journal of Computational Physics, 2019 | 8 | 2019 |
| Visual Imitation with a Minimal Adversary S Reed, Y Aytar, Z Wang, T Paine, A van den Oord, T Pfaff, S Gomez, ... | 2 | 2018 |
| Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds A Novikov, M Rakhuba, I Oseledets arXiv preprint arXiv:2103.14974, 2021 | | 2021 |
| Action selection neural network training using imitation learning in latent space SE Reed, Y Aytar, Z Wang, T Paine, SG Colmenarejo, D Budden, T Pfaff, ... US Patent App. 16/586,437, 2020 | | 2020 |
Dmitry VetrovHigher School of Economics, Samsung AI Center, MoscowVerified email at hse.ru
