Sylvain Gelly
Sylvain Gelly
Google Brain Zurich
Verified email at
Cited by
Cited by
An image is worth 16x16 words: Transformers for image recognition at scale
A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ...
arXiv preprint arXiv:2010.11929, 2020
Parameter-efficient transfer learning for NLP
N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q De Laroussilhe, ...
International conference on machine learning, 2790-2799, 2019
Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, G Raetsch, S Gelly, B Schölkopf, O Bachem
international conference on machine learning, 4114-4124, 2019
Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 2017
Big transfer (bit): General visual representation learning
A Kolesnikov, L Beyer, X Zhai, J Puigcerver, J Yung, S Gelly, N Houlsby
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
Are gans created equal? a large-scale study
M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet
Advances in neural information processing systems 31, 2018
Combining online and offline knowledge in UCT
S Gelly, D Silver
Proceedings of the 24th international conference on Machine learning, 273-280, 2007
Assessing generative models via precision and recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in neural information processing systems 31, 2018
Modification of UCT with Patterns in Monte-Carlo Go
S Gelly, Y Wang, R Munos, O Teytaud
INRIA, 2006
On mutual information maximization for representation learning
M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic
arXiv preprint arXiv:1907.13625, 2019
Monte-Carlo tree search and rapid action value estimation in computer Go
S Gelly, D Silver
Artificial Intelligence 175 (11), 1856-1875, 2011
Towards accurate generative models of video: A new metric & challenges
T Unterthiner, S Van Steenkiste, K Kurach, R Marinier, M Michalski, ...
arXiv preprint arXiv:1812.01717, 2018
Google research football: A novel reinforcement learning environment
K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ...
Proceedings of the AAAI conference on artificial intelligence 34 (04), 4501-4510, 2020
Episodic curiosity through reachability
N Savinov, A Raichuk, R Marinier, D Vincent, M Pollefeys, T Lillicrap, ...
arXiv preprint arXiv:1810.02274, 2018
The grand challenge of computer Go: Monte Carlo tree search and extensions
S Gelly, L Kocsis, M Schoenauer, M Sebag, D Silver, C Szepesvári, ...
Communications of the ACM 55 (3), 106-113, 2012
Exploration exploitation in go: UCT for Monte-Carlo go
S Gelly, Y Wang
NIPS: Neural Information Processing Systems Conference On-line trading of …, 2006
A large-scale study of representation learning with the visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
arXiv preprint arXiv:1910.04867, 2019
Adagan: Boosting generative models
IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf
Advances in neural information processing systems 30, 2017
What matters in on-policy reinforcement learning? a large-scale empirical study
M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ...
arXiv preprint arXiv:2006.05990, 2020
A large-scale study on regularization and normalization in GANs
K Kurach, M Lučić, X Zhai, M Michalski, S Gelly
International conference on machine learning, 3581-3590, 2019
The system can't perform the operation now. Try again later.
Articles 1–20