| Revisiting deep learning models for tabular data Y Gorishniy, I Rubachev, V Khrulkov, A Babenko Advances in Neural Information Processing Systems 34, 18932-18943, 2021 | 794 | 2021 |
| Label-efficient semantic segmentation with diffusion models D Baranchuk, I Rubachev, A Voynov, V Khrulkov, A Babenko arXiv preprint arXiv:2112.03126, 2021 | 547 | 2021 |
| Hyperbolic image embeddings V Khrulkov, L Mirvakhabova, E Ustinova, I Oseledets, V Lempitsky Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 344 | 2020 |
| Art of singular vectors and universal adversarial perturbations V Khrulkov, I Oseledets The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 …, 2017 | 162 | 2017 |
| Geometry Score: A Method For Comparing Generative Adversarial Networks V Khrulkov, I Oseledets ICML 2018, 2018 | 145 | 2018 |
| Expressive power of recurrent neural networks V Khrulkov, A Novikov, I Oseledets International Conference on Learning Representations (ICLR) 2018, 2017 | 131 | 2017 |
| Hyperbolic vision transformers: Combining improvements in metric learning A Ermolov, L Mirvakhabova, V Khrulkov, N Sebe, I Oseledets Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 117 | 2022 |
| Tensorized Embedding Layers for Efficient Model Compression V Khrulkov, O Hrinchuk, L Mirvakhabova, E Orlova, I Oseledets arXiv preprint arXiv:1901.10787, 2019 | 116* | 2019 |
| Tensor train decomposition on tensorflow (t3f) A Novikov, P Izmailov, V Khrulkov, M Figurnov, I Oseledets Journal of Machine Learning Research 21 (30), 1-7, 2020 | 75 | 2020 |
| Understanding ddpm latent codes through optimal transport V Khrulkov, G Ryzhakov, A Chertkov, I Oseledets arXiv preprint arXiv:2202.07477, 2022 | 56 | 2022 |
| Performance of hyperbolic geometry models on top-n recommendation tasks L Mirvakhabova, E Frolov, V Khrulkov, I Oseledets, A Tuzhilin Proceedings of the 14th ACM Conference on Recommender Systems, 527-532, 2020 | 38 | 2020 |
| Generalized tensor models for recurrent neural networks V Khrulkov, O Hrinchuk, I Oseledets arXiv preprint arXiv:1901.10801, 2019 | 28 | 2019 |
| Disentangled representations from non-disentangled models V Khrulkov, L Mirvakhabova, I Oseledets, A Babenko arXiv preprint arXiv:2102.06204, 2021 | 19 | 2021 |
| Latent transformations via NeuralODEs for GAN-based image editing V Khrulkov, L Mirvakhabova, I Oseledets, A Babenko Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 19 | 2021 |
| Neural side-by-side: Predicting human preferences for no-reference super-resolution evaluation V Khrulkov, A Babenko Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 17 | 2021 |
| Desingularization of bounded-rank matrix sets V Khrulkov, I Oseledets SIAM Journal on Matrix Analysis and Applications 39 (1), 451-471, 2018 | 12 | 2018 |
| Functional space analysis of local GAN convergence V Khrulkov, A Babenko, I Oseledets International Conference on Machine Learning, 5432-5442, 2021 | 5 | 2021 |
| Sample efficient ensemble learning with catalyst. rl S Kolesnikov, V Khrulkov arXiv preprint arXiv:2003.14210, 2020 | 5 | 2020 |
| Geometrical Methods in Machine Learning and Tensor Analysis V Khrulkov PhD dissertation, Skolkovo Institute, 2020 | 4 | 2020 |
| Universality theorems for generative models V Khrulkov, I Oseledets arXiv preprint arXiv:1905.11520, 2019 | 3 | 2019 |
Ivan OseledetsAIRI; Skolkovo Institute of Science and TechnologyBestätigte E-Mail-Adresse bei skoltech.ru
