| Learning and transferring mid-level image representations using convolutional neural networks M Oquab, L Bottou, I Laptev, J Sivic Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 4128 | 2014 |
| Dinov2: Learning robust visual features without supervision M Oquab, T Darcet, T Moutakanni, H Vo, M Szafraniec, V Khalidov, ... arXiv preprint arXiv:2304.07193, 2023 | 2206* | 2023 |
| Is object localization for free?-weakly-supervised learning with convolutional neural networks M Oquab, L Bottou, I Laptev, J Sivic Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1163 | 2015 |
| Revisiting classifier two-sample tests D Lopez-Paz, M Oquab arXiv preprint arXiv:1610.06545, 2016 | 475 | 2016 |
| Contextlocnet: Context-aware deep network models for weakly supervised localization V Kantorov, M Oquab, M Cho, I Laptev Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 363 | 2016 |
| Vision transformers need registers T Darcet, M Oquab, J Mairal, P Bojanowski arXiv preprint arXiv:2309.16588, 2023 | 191 | 2023 |
| Low bandwidth video-chat compression using deep generative models M Oquab, P Stock, D Haziza, T Xu, P Zhang, O Celebi, Y Hasson, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 50 | 2021 |
| Geometrical insights for implicit generative modeling L Bottou, M Arjovsky, D Lopez-Paz, M Oquab Braverman Readings in Machine Learning. Key Ideas from Inception to Current …, 2018 | 37 | 2018 |
| Learning about an exponential amount of conditional distributions M Belghazi, M Oquab, D Lopez-Paz Advances in Neural Information Processing Systems 32, 2019 | 31 | 2019 |
| Can RNNs learn recursive nested subject-verb agreements? Y Lakretz, T Desbordes, JR King, B Crabbé, M Oquab, S Dehaene arXiv preprint arXiv:2101.02258, 2021 | 21 | 2021 |
| Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations JR King, F Charton, D Lopez-Paz, M Oquab NeuroImage 220, 117028, 2020 | 20 | 2020 |
| Geometrical insights for implicit generative modeling L Bottou, M Arjovsky, D Lopez-Paz, M Oquab arXiv preprint arXiv:1712.07822, 2017 | 15 | 2017 |
| Dimensionality and ramping: Signatures of sentence integration in the dynamics of brains and deep language models T Desbordes, Y Lakretz, V Chanoine, M Oquab, JM Badier, A Trébuchon, ... Journal of Neuroscience 43 (29), 5350-5364, 2023 | 12 | 2023 |
| Co-training 2L submodels for visual recognition H Touvron, M Cord, M Oquab, P Bojanowski, J Verbeek, H Jégou Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 10 | 2023 |
| Self-appearance-aided differential evolution for motion transfer P Liu, R Wang, X Cao, Y Zhou, A Shah, M Oquab, C Couprie, SN Lim arXiv e-prints, arXiv: 2110.04658, 2021 | 8 | 2021 |
| Consistent population control: generate plenty of points, but with a bit of resampling V Khalidov, M Oquab, J Rapin, O Teytaud Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic …, 2019 | 4 | 2019 |
| Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach HV Vo, V Khalidov, T Darcet, T Moutakanni, N Smetanin, M Szafraniec, ... arXiv preprint arXiv:2405.15613, 2024 | 3 | 2024 |
| Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning T Moutakanni, P Bojanowski, G Chassagnon, C Hudelot, A Joulin, ... arXiv preprint arXiv:2405.01469, 2024 | 3 | 2024 |
| Discriminating the influence of correlated factors from multivariate observations: the back-to-back regression JR King, F Charton, D Lopez-Paz, M Oquab bioRxiv, 2020.03. 05.976936, 2020 | 3 | 2020 |
| You Don't Need Data-Augmentation in Self-Supervised Learning T Moutakanni, M Oquab, M Szafraniec, M Vakalopoulou, P Bojanowski arXiv preprint arXiv:2406.09294, 2024 | 2 | 2024 |
