| Variational dropout sparsifies deep neural networks D Molchanov, A Ashukha, D Vetrov International conference on machine learning, 2498-2507, 2017 | 1087 | 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 | 372 | 2020 |
| Structured bayesian pruning via log-normal multiplicative noise K Neklyudov, D Molchanov, A Ashukha, DP Vetrov Advances in Neural Information Processing Systems 30, 2017 | 234 | 2017 |
| Greedy policy search: A simple baseline for learnable test-time augmentation A Lyzhov, Y Molchanova, A Ashukha, D Molchanov, D Vetrov Conference on uncertainty in artificial intelligence, 1308-1317, 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 | 61 | 2019 |
| Doubly semi-implicit variational inference D Molchanov, V Kharitonov, A Sobolev, D Vetrov The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 55 | 2019 |
| Variance networks: When expectation does not meet your expectations K Neklyudov, D Molchanov, A Ashukha, D Vetrov arXiv preprint arXiv:1803.03764, 2018 | 35 | 2018 |
| Bayesian incremental learning for deep neural networks M Kochurov, T Garipov, D Podoprikhin, D Molchanov, A Ashukha, ... arXiv preprint arXiv:1802.07329, 2018 | 23 | 2018 |
| Variational dropout via empirical bayes V Kharitonov, D Molchanov, D Vetrov arXiv preprint arXiv:1811.00596, 5, 2018 | 15 | 2018 |
| Star-shaped denoising diffusion probabilistic models A Okhotin, D Molchanov, A Vladimir, G Bartosh, V Ohanesian, A Alanov, ... Advances in Neural Information Processing Systems 36, 2024 | 8 | 2024 |
| Dropout-based automatic relevance determination D Molchanov, A Ashuha, D Vetrov Bayesian Deep Learning workshop, NIPS, 2016 | 3 | 2016 |
| Relevance tagging machine DA Molchanov, DA Kondrashkin, DP Vetrov Machine Learning and Data Analysis 1 (13), 1877-1887, 2015 | 1 | 2015 |
| TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings A Shabalin, V Meshchaninov, E Chimbulatov, V Lapikov, R Kim, ... arXiv preprint arXiv:2402.19097, 2024 | | 2024 |
| Probabilistic Models A Okhotin, D Molchanov, V Arkhipkin, D Vetrov KI 2024: Advances in Artificial Intelligence: 47th German Conference on AI …, 2024 | | 2024 |
| Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks V Yanush, A Shekhovtsov, D Molchanov, D Vetrov arXiv e-prints, arXiv: 2006.06880, 2020 | | 2020 |
| Structured Semi-Implicit Variational Inference I Molchanova, D Molchanov, N Quadrianto, D Vetrov Second Symposium on Advances in Approximate Bayesian Inference, 0 | | |
Dmitry VetrovProfessor of Computer Science at Constructor University, BremenVerified email at constructor.university
