| Overfeat: Integrated Recognition, Localization and Detection Using Convolutional networks P Sermanet arXiv preprint arXiv:1312.6229, 2013 | 7852 | 2013 |
| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... nature 575 (7782), 350-354, 2019 | 5195* | 2019 |
| Deep multi-scale video prediction beyond mean square error M Mathieu, C Couprie, Y LeCun arXiv preprint arXiv:1511.05440, 2015 | 2351 | 2015 |
| The loss surfaces of multilayer networks A Choromanska, M Henaff, M Mathieu, GB Arous, Y LeCun Artificial intelligence and statistics, 192-204, 2015 | 1641 | 2015 |
| Energy-based Generative Adversarial Network J Zhao arXiv preprint arXiv:1609.03126, 2016 | 1616 | 2016 |
| Fast training of convolutional networks through ffts M Mathieu, M Henaff, Y LeCun arXiv preprint arXiv:1312.5851, 2013 | 818 | 2013 |
| Learning convolutional feature hierarchies for visual recognition K Kavukcuoglu, P Sermanet, YL Boureau, K Gregor, M Mathieu, Y Cun Advances in neural information processing systems 23, 2010 | 751 | 2010 |
| Disentangling factors of variation in deep representation using adversarial training MF Mathieu, JJ Zhao, J Zhao, A Ramesh, P Sprechmann, Y LeCun Advances in neural information processing systems 29, 2016 | 563 | 2016 |
| Video (language) modeling: a baseline for generative models of natural videos MA Ranzato, A Szlam, J Bruna, M Mathieu, R Collobert, S Chopra arXiv preprint arXiv:1412.6604, 2014 | 537 | 2014 |
| Fast convolutional nets with fbfft: A GPU performance evaluation N Vasilache, J Johnson, M Mathieu, S Chintala, S Piantino, Y LeCun arXiv preprint arXiv:1412.7580, 2014 | 412 | 2014 |
| Stacked what-where auto-encoders J Zhao, M Mathieu, R Goroshin, Y Lecun arXiv preprint arXiv:1506.02351, 2015 | 351 | 2015 |
| Learning longer memory in recurrent neural networks T Mikolov, A Joulin, S Chopra, M Mathieu, MA Ranzato arXiv preprint arXiv:1412.7753, 2014 | 332 | 2014 |
| Open-ended learning leads to generally capable agents OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ... arXiv preprint arXiv:2107.12808, 2021 | 159 | 2021 |
| Learning to linearize under uncertainty R Goroshin, MF Mathieu, Y LeCun Advances in neural information processing systems 28, 2015 | 148 | 2015 |
| How to train a GAN? Tips and tricks to make GANs work S Chintala, E Denton, M Arjovsky, M Mathieu GitHub, Dec, 2016 | 132 | 2016 |
| Deep multi-scale video prediction beyond mean square error. arXiv 2015 M Mathieu, C Couprie, Y Lecun arXiv preprint arXiv:1511.05440, 2015 | 53 | 2015 |
| Fast approximation of rotations and Hessians matrices M Mathieu, Y LeCun arXiv preprint arXiv:1404.7195, 2014 | 28 | 2014 |
| Starcraft ii unplugged: Large scale offline reinforcement learning M Mathieu, S Ozair, S Srinivasan, C Gulcehre, S Zhang, R Jiang, ... Deep RL Workshop NeurIPS 2021, 2021 | 19 | 2021 |
| Alphastar unplugged: Large-scale offline reinforcement learning M Mathieu, S Ozair, S Srinivasan, C Gulcehre, S Zhang, R Jiang, ... arXiv preprint arXiv:2308.03526, 2023 | 8 | 2023 |
| Unsupervised Learning Under Uncertainty M Mathieu New York University, 2017 | 3 | 2017 |
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityAdresse e-mail validée de cs.nyu.edu
