| On power laws in deep ensembles E Lobacheva, N Chirkova, M Kodryan, DP Vetrov Advances in Neural Information Processing Systems 33, 2375-2385, 2020 | 47 | 2020 |
| On the periodic behavior of neural network training with batch normalization and weight decay E Lobacheva, M Kodryan, N Chirkova, A Malinin, DP Vetrov Advances in Neural Information Processing Systems 34, 21545-21556, 2021 | 25 | 2021 |
| Bayesian sparsification of recurrent neural networks E Lobacheva, N Chirkova, D Vetrov arXiv preprint arXiv:1708.00077, 2017 | 19 | 2017 |
| Bayesian compression for natural language processing N Chirkova, E Lobacheva, D Vetrov arXiv preprint arXiv:1810.10927, 2018 | 16 | 2018 |
| Automated real-time classification of functional states based on physiological parameters EM Lobacheva, YN Galatenko, RF Gabidullina, VV Galatenko, ED Livshitz, ... Procedia-Social and Behavioral Sciences 86, 373-378, 2013 | 10 | 2013 |
| Training scale-invariant neural networks on the sphere can happen in three regimes M Kodryan, E Lobacheva, M Nakhodnov, DP Vetrov Advances in Neural Information Processing Systems 35, 14058-14070, 2022 | 9 | 2022 |
| Deep ensembles on a fixed memory budget: One wide network or several thinner ones? N Chirkova, E Lobacheva, D Vetrov arXiv preprint arXiv:2005.07292, 2020 | 9 | 2020 |
| Semantic embeddings for program behavior patterns A Chistyakov, E Lobacheva, A Kuznetsov, A Romanenko arXiv preprint arXiv:1804.03635, 2018 | 9 | 2018 |
| Structured sparsification of gated recurrent neural networks E Lobacheva, N Chirkova, A Markovich, D Vetrov Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4989-4996, 2020 | 8 | 2020 |
| automated real-time classification of functional states: significance of individual tuning stage MG Ya, E Podol’Skii Vladimir Psychology in Russia: State of the art 6 (3), 41-48, 2013 | 8 | 2013 |
| Joint optimization of segmentation and color clustering E Lobacheva, O Veksler, Y Boykov Proceedings of the IEEE International Conference on Computer Vision, 1626-1634, 2015 | 7 | 2015 |
| Bayesian sparsification of gated recurrent neural networks E Lobacheva, N Chirkova, D Vetrov arXiv preprint arXiv:1812.05692, 2018 | 4 | 2018 |
| To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning I Sadrtdinov, D Pozdeev, DP Vetrov, E Lobacheva Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Loss function dynamics and landscape for deep neural networks trained with quadratic loss MS Nakhodnov, MS Kodryan, EM Lobacheva, DS Vetrov Doklady Mathematics 106 (Suppl 1), S43-S62, 2022 | 3 | 2022 |
| Deep part-based generative shape model with latent variables A Kirillov, M Gavrikov, E Lobacheva, A Osokin, D Vetrov 27th British Machine Vision Conference (BMVC 2016), 2016 | 3 | 2016 |
| Monotonic models for real-time dynamic malware detection A Chistyakov, E Lobacheva, A Shevelev, A Romanenko arXiv preprint arXiv:1804.03643, 2018 | 2 | 2018 |
| System and method of allocating computer resources for detection of malicious files AC Chistyakov, EM Lobacheva, AM Romanenko US Patent 11,403,396, 2022 | 1 | 2022 |
| On the memorization properties of contrastive learning I Sadrtdinov, N Chirkova, E Lobacheva arXiv preprint arXiv:2107.10143, 2021 | 1 | 2021 |
| Adaptive prediction time for sequence classification M Ryabinin, E Lobacheva | 1 | 2018 |
| Large Learning Rates Improve Generalization: But How Large Are We Talking About? E Lobacheva, E Pockonechnyy, M Kodryan, D Vetrov arXiv preprint arXiv:2311.11303, 2023 | | 2023 |
Dmitry VetrovProfessor of Computer Science at Constructor University, Bremen在 constructor.university 的电子邮件经过验证
