Παρακολούθηση
Maxime Oquab
Maxime Oquab
Facebook AI Research
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα fb.com
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Παρατίθεται από
Παρατίθεται από
Έτος
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
36242014
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
10142015
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
3112016
Revisiting classifier two-sample tests
D Lopez-Paz, M Oquab
arXiv preprint arXiv:1610.06545, 2016
2982016
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
312018
Learning about an exponential amount of conditional distributions
M Belghazi, M Oquab, D Lopez-Paz
Advances in Neural Information Processing Systems 32, 2019
252019
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
212021
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
162021
Geometrical insights for implicit generative modeling
L Bottou, M Arjovsky, D Lopez-Paz, M Oquab
arXiv preprint arXiv:1712.07822, 2017
152017
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
102020
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
32020
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
22021
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
12023
Co-training Submodels for Visual Recognition
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
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