| 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 | 3700 | 2014 |
| 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 | 1032 | 2015 |
| 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 | 322 | 2016 |
| Revisiting classifier two-sample tests D Lopez-Paz, M Oquab arXiv preprint arXiv:1610.06545, 2016 | 319 | 2016 |
| 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 | 31 | 2018 |
| 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 | 26 | 2021 |
| Learning about an exponential amount of conditional distributions M Belghazi, M Oquab, D Lopez-Paz Advances in Neural Information Processing Systems 32, 2019 | 26 | 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 | 18 | 2021 |
| Geometrical insights for implicit generative modeling L Bottou, M Arjovsky, D Lopez-Paz, M Oquab arXiv preprint arXiv:1712.07822, 2017 | 15 | 2017 |
| 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 | 12 | 2020 |
| 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 | 7 | 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 preprint arXiv:2110.04658, 2021 | 4 | 2021 |
| 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 |
| 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, 2023 | 1 | 2023 |
| Systems and method for low bandwidth video-chat compression MM Oquab, P Stock, O Gafni, DRD Haziza, T Xu, P Zhang, O Çelebi, ... US Patent App. 17/224,103, 2022 | 1 | 2022 |
| 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 | 1 | 2019 |
| 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 | | 2023 |
| Efficient conditioned face animation using frontally-viewed embedding M Oquab, D Haziza, L Schwartz, T Xu, K Zand, R Wang, P Liu, C Couprie arXiv preprint arXiv:2203.08765, 2022 | | 2022 |
| Learning about an exponential amount of conditional distributions M Ishmael Belghazi, M Oquab, Y LeCun, D Lopez-Paz arXiv e-prints, arXiv: 1902.08401, 2019 | | 2019 |
| Convolutional neural networks: towards less supervision for visual recognition M Oquab Paris Sciences et Lettres (ComUE), 2018 | | 2018 |
