Michaël Mathieu

According to our database, Michaël Mathieu authored at least 21 papers between 2010 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning.

Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Çaglar Gülçehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad Zolna, Julian Schrittwieser, David H. Choi, Petko Georgiev, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals

CoRR, 2023

2021

Open-Ended Learning Leads to Generally Capable Agents.

Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michaël Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, Wojciech Marian Czarnecki

CoRR, 2021

2019

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver

Nat., 2019

2017

Unsupervised Learning Under Uncertainty.

Michaël Mathieu

PhD thesis, 2017

Energy-based Generative Adversarial Networks.

Junbo Jake Zhao, Michaël Mathieu, Yann LeCun

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Energy-based Generative Adversarial Network.

Junbo Jake Zhao, Michaël Mathieu, Yann LeCun

CoRR, 2016

Disentangling factors of variation in deep representations using adversarial training.

Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun

CoRR, 2016

Deep multi-scale video prediction beyond mean square error.

Michaël Mathieu, Camille Couprie, Yann LeCun

Proceedings of the 4th International Conference on Learning Representations, 2016

Disentangling factors of variation in deep representation using adversarial training.

Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Stacked What-Where Auto-encoders.

Junbo Jake Zhao, Michaël Mathieu, Ross Goroshin, Yann LeCun

CoRR, 2015

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation.

Nicolas Vasilache, Jeff Johnson, Michaël Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

Proceedings of the 3rd International Conference on Learning Representations, 2015

Learning Longer Memory in Recurrent Neural Networks.

Tomás Mikolov, Armand Joulin, Sumit Chopra, Michaël Mathieu, Marc'Aurelio Ranzato

Proceedings of the 3rd International Conference on Learning Representations, 2015

Learning to Linearize Under Uncertainty.

Ross Goroshin, Michaël Mathieu, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

The Loss Surfaces of Multilayer Networks.

Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, Yann LeCun

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks.

Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, Yann LeCun

Proceedings of the 2nd International Conference on Learning Representations, 2014

Video (language) modeling: a baseline for generative models of natural videos.

Marc'Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michaël Mathieu, Ronan Collobert, Sumit Chopra

CoRR, 2014

Fast Approximation of Rotations and Hessians matrices.

Michaël Mathieu, Yann LeCun

CoRR, 2014

Fast Training of Convolutional Networks through FFTs.

Michaël Mathieu, Mikael Henaff, Yann LeCun

Proceedings of the 2nd International Conference on Learning Representations, 2014

The Loss Surface of Multilayer Networks.

Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, Yann LeCun

CoRR, 2014

2012

Physical Simulation of Inarticulate Robots.

Guillaume Claret, Michaël Mathieu, David Naccache, Guillaume Seguin

Proceedings of the Cryptography and Security: From Theory to Applications, 2012

2010

Learning Convolutional Feature Hierarchies for Visual Recognition.

Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michaël Mathieu, Yann LeCun

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010