| Variational Dropout Sparsifies Deep Neural Networks D Molchanov*, A Ashukha*, D Vetrov Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 2017 | 1089 | 2017 |
| Resolution-robust large mask inpainting with fourier convolutions R Suvorov, E Logacheva, A Mashikhin, A Remizova, A Ashukha, ... Proceedings of the IEEE/CVF winter conference on applications of computer …, 2022 | 861 | 2022 |
| Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning A Ashukha, A Lyzhov, D Molchanov, D Vetrov International Conference on Learning Representations (ICLR 2020), 2020 | 372 | 2020 |
| Structured Bayesian Pruning via Log-Normal Multiplicative Noise K Neklyudov, D Molchanov, A Ashukha, D Vetrov Advances in Neural Information Processing Systems 30 (NIPS 2017), 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 International Conference on Learning Representations, Workshop Track (ICLR 2018), 2018 | 61 | 2018 |
| The Deep Weight Prior A Atanov*, A Ashukha*, K Struminsky, D Vetrov, M Welling International Conference on Learning Representations (ICLR 2019), 2018 | 44 | 2018 |
| Semi-conditional normalizing flows for semi-supervised learning A Atanov, A Volokhova, A Ashukha, I Sosnovik, D Vetrov arXiv preprint arXiv:1905.00505 920, 2019 | 36 | 2019 |
| Variance Networks: When Expectation Does Not Meet Your Expectations K Neklyudov*, D Molchanov*, A Ashukha*, D Vetrov International Conference on Learning Representations (ICLR 2019), 2018 | 35 | 2018 |
| Bayesian Incremental Learning for Deep Neural Networks M Kochurov, T Garipov, D Podoprikhin, D Molchanov, A Ashukha, ... International Conference on Learning Representations, Workshop Track (ICLR 2018), 2018 | 23 | 2018 |
| Automating control of overestimation bias for reinforcement learning A Kuznetsov, A Grishin, A Tsypin, A Ashukha, A Kadurin, D Vetrov arXiv preprint arXiv:2110.13523, 2021 | 13 | 2021 |
| Mean embeddings with test-time data augmentation for ensembling of representations A Ashukha, A Atanov, D Vetrov arXiv preprint arXiv:2106.08038, 2021 | 4 | 2021 |
| Dropout-based automatic relevance determination D Molchanov, A Ashuha, D Vetrov Bayesian Deep Learning Workshop (NeurIPS 2016), 2016 | 3 | 2016 |
| A glimpse of the next generation of AlphaFold GoogleDeepMind, IsomorphicLabs https://www.isomorphiclabs.com/articles/a-glimpse-of-the-next-generation-of …, 2023 | | 2023 |
| Unsupervised Domain Adaptation with Shared Latent Dynamics for Reinforcement Learning E Nikishin, A Ashukha, D Vetrov Bayesian Deep Learning Workshop (NeurIPS 2019), 2019 | | 2019 |
Dmitry VetrovProfessor of Computer Science at Constructor University, BremenZweryfikowany adres z constructor.university
