| Tensorizing neural networks A Novikov, D Podoprikhin, A Osokin, DP Vetrov Advances in neural information processing systems, 442-450, 2015 | 365 | 2015 |
| 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 | 328 | 2006 |
| Variational dropout sparsifies deep neural networks D Molchanov, A Ashukha, D Vetrov Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 269 | 2017 |
| 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 | 134 | 2018 |
| Spatially Adaptive Computation Time for Residual Networks M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ... | 120 | 2017 |
| Breaking sticks and ambiguities with adaptive skip-gram S Bartunov, D Kondrashkin, A Osokin, D Vetrov artificial intelligence and statistics, 130-138, 2016 | 104 | 2016 |
| Perforatedcnns: Acceleration through elimination of redundant convolutions M Figurnov, A Ibraimova, DP Vetrov, P Kohli Advances in Neural Information Processing Systems, 947-955, 2016 | 91 | 2016 |
| 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, 8789-8798, 2018 | 80 | 2018 |
| Structured bayesian pruning via log-normal multiplicative noise K Neklyudov, D Molchanov, A Ashukha, DP Vetrov Advances in Neural Information Processing Systems, 6775-6784, 2017 | 79 | 2017 |
| Ultimate tensorization: compressing convolutional and fc layers alike T Garipov, D Podoprikhin, A Novikov, D Vetrov arXiv preprint arXiv:1611.03214, 2016 | 57 | 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 | 53 | 2018 |
| Spatial inference machines R Shapovalov, D Vetrov, P Kohli Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 42 | 2013 |
| 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, 13132-13143, 2019 | 36 | 2019 |
| Inferring M-best diverse labelings in a single one A Kirillov, B Savchynskyy, D Schlesinger, D Vetrov, C Rother Proceedings of the IEEE International Conference on Computer Vision, 1814-1822, 2015 | 35 | 2015 |
| Fast adaptation in generative models with generative matching networks S Bartunov, DP Vetrov arXiv preprint arXiv:1612.02192, 2016 | 30* | 2016 |
| Putting MRFs on a tensor train A Novikov, A Rodomanov, A Osokin, D Vetrov International Conference on Machine Learning, 811-819, 2014 | 21 | 2014 |
| Variational autoencoder with arbitrary conditioning O Ivanov, M Figurnov, D Vetrov arXiv preprint arXiv:1806.02382, 2018 | 20 | 2018 |
| Submodular decomposition framework for inference in associative markov networks with global constraints A Osokin, D Vetrov, V Kolmogorov CVPR 2011, 1889-1896, 2011 | 20 | 2011 |
| M-best-diverse labelings for submodular energies and beyond A Kirillov, D Shlezinger, DP Vetrov, C Rother, B Savchynskyy Advances in Neural Information Processing Systems, 613-621, 2015 | 19 | 2015 |
| Uncertainty estimation via stochastic batch normalization A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov International Symposium on Neural Networks, 261-269, 2019 | 17 | 2019 |
Dmitry KropotovLomonosov Moscow State UniversityBestätigte E-Mail-Adresse bei cs.msu.ru
