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
3632 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
1017 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
311 2016 Revisiting classifier two-sample tests D Lopez-Paz, M Oquab
arXiv preprint arXiv:1610.06545, 2016
300 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 Learning about an exponential amount of conditional distributions M Belghazi, M Oquab, D Lopez-Paz
Advances in Neural Information Processing Systems 32, 2019
25 2019 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
21 2021 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
16 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
10 2020 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
3 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 Trebuchon, ...
bioRxiv, 2023.02. 28.530443, 2023
1 2023 Co-training H Touvron, M Cord, M Oquab, P Bojanowski, J Verbeek, H Jégou
arXiv preprint arXiv:2212.04884, 2022
2022 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
2022 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 Multimodal Noise and Covering Initializations for GANs D Lopez-Paz, M Oquab
Measuring causal influence with back-to-back regression: the linear case JR King, F Charton, M Oquab, D Lopez-Paz
