| Multimae: Multi-modal multi-task masked autoencoders R Bachmann, D Mizrahi, A Atanov, A Zamir Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022 | 44 | 2022 |
| The deep weight prior A Atanov, A Ashukha, K Struminsky, D Vetrov, M Welling arXiv preprint arXiv:1810.06943, 2018 | 36 | 2018 |
| Uncertainty estimation via stochastic batch normalization A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov arXiv preprint arXiv:1802.04893, 2018 | 31 | 2018 |
| Semi-conditional normalizing flows for semi-supervised learning A Atanov, A Volokhova, A Ashukha, I Sosnovik, D Vetrov arXiv preprint arXiv:1905.00505, 2019 | 24 | 2019 |
| 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 | 24 | 2019 |
| 3d common corruptions and data augmentation OF Kar, T Yeo, A Atanov, A Zamir Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 10 | 2022 |
| Mean embeddings with test-time data augmentation for ensembling of representations A Ashukha, A Atanov, D Vetrov arXiv preprint arXiv:2106.08038, 2021 | 3 | 2021 |
| Task Discovery: Finding the Tasks that Neural Networks Generalize on A Atanov, A Filatov, T Yeo, A Sohmshetty, A Zamir arXiv preprint arXiv:2212.00261, 2022 | 1 | 2022 |
| Simple control baselines for evaluating transfer learning A Atanov, S Xu, O Beker, A Filatov, A Zamir arXiv preprint arXiv:2202.03365, 2022 | 1 | 2022 |
| Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves A Atanov, S Xu, O Beker, A Filatov, A Zamir | | |
| Reproducibility Summary A Atanov, V Shumovskaia, M Vujasinovic | | |
Arsenii AshukhaReimagining drug discovery @ Isomorphic Labs 💊 🧬Verified email at google.com
