| Averaging weights leads to wider optima and better generalization P Izmailov, D Podoprikhin, T Garipov, D Vetrov, AG Wilson arXiv preprint arXiv:1803.05407, 2018 | 1013 | 2018 |
| Variational dropout sparsifies deep neural networks D Molchanov, A Ashukha, D Vetrov International Conference on Machine Learning, 2498-2507, 2017 | 837 | 2017 |
| Tensorizing neural networks A Novikov, D Podoprikhin, A Osokin, DP Vetrov Advances in neural information processing systems 28, 2015 | 837 | 2015 |
| A simple baseline for bayesian uncertainty in deep learning WJ Maddox, P Izmailov, T Garipov, DP Vetrov, AG Wilson Advances in neural information processing systems 32, 2019 | 583 | 2019 |
| Loss surfaces, mode connectivity, and fast ensembling of dnns T Garipov, P Izmailov, D Podoprikhin, DP Vetrov, AG Wilson Advances in neural information processing systems 31, 2018 | 480 | 2018 |
| Evaluation of stability of k-means cluster ensembles with respect to random initialization LI Kuncheva, DP Vetrov IEEE transactions on pattern analysis and machine intelligence 28 (11), 1798 …, 2006 | 389 | 2006 |
| Spatially Adaptive Computation Time for Residual Networks M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ... | 324 | 2017 |
| Pitfalls of in-domain uncertainty estimation and ensembling in deep learning A Ashukha, A Lyzhov, D Molchanov, D Vetrov arXiv preprint arXiv:2002.06470, 2020 | 233 | 2020 |
| Breaking sticks and ambiguities with adaptive skip-gram S Bartunov, D Kondrashkin, A Osokin, D Vetrov artificial intelligence and statistics, 130-138, 2016 | 201 | 2016 |
| Structured bayesian pruning via log-normal multiplicative noise K Neklyudov, D Molchanov, A Ashukha, DP Vetrov Advances in Neural Information Processing Systems 30, 2017 | 192 | 2017 |
| Ultimate tensorization: compressing convolutional and fc layers alike T Garipov, D Podoprikhin, A Novikov, D Vetrov arXiv preprint arXiv:1611.03214, 2016 | 183 | 2016 |
| Perforatedcnns: Acceleration through elimination of redundant convolutions M Figurnov, A Ibraimova, DP Vetrov, P Kohli Advances in neural information processing systems 29, 2016 | 180 | 2016 |
| Entangled conditional adversarial autoencoder for de novo drug discovery D Polykovskiy, A Zhebrak, D Vetrov, Y Ivanenkov, V Aladinskiy, ... Molecular pharmaceutics 15 (10), 4398-4405, 2018 | 177 | 2018 |
| Variational autoencoder with arbitrary conditioning O Ivanov, M Figurnov, D Vetrov arXiv preprint arXiv:1806.02382, 2018 | 125 | 2018 |
| Subspace inference for Bayesian deep learning P Izmailov, WJ Maddox, P Kirichenko, T Garipov, D Vetrov, AG Wilson Uncertainty in Artificial Intelligence, 1169-1179, 2020 | 110 | 2020 |
| Fast adaptation in generative models with generative matching networks S Bartunov, DP Vetrov arXiv preprint arXiv:1612.02192, 2016 | 107* | 2016 |
| Controlling overestimation bias with truncated mixture of continuous distributional quantile critics A Kuznetsov, P Shvechikov, A Grishin, D Vetrov International Conference on Machine Learning, 5556-5566, 2020 | 83 | 2020 |
| Uncertainty estimation via stochastic batch normalization A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov Advances in Neural Networks–ISNN 2019: 16th International Symposium on …, 2019 | 51 | 2019 |
| Conditional generators of words definitions A Gadetsky, I Yakubovskiy, D Vetrov arXiv preprint arXiv:1806.10090, 2018 | 50 | 2018 |
| Putting MRFs on a tensor train A Novikov, A Rodomanov, A Osokin, D Vetrov International Conference on Machine Learning, 811-819, 2014 | 50 | 2014 |
Anton OsokinIsomorphic LabsVerified email at isomorphiclabs.com
