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 | 11336 | 2020 |
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 | 1007 | 2019 |
Wasserstein auto-encoders I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf arXiv preprint arXiv:1711.01558, 2017 | 948 | 2017 |
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 | 907 | 2019 |
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 | 894 | 2018 |
Combining online and offline knowledge in UCT S Gelly, D Silver Proceedings of the 24th international conference on Machine learning, 273-280, 2007 | 736 | 2007 |
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 | 685 | 2020 |
An image is worth 16x16 words: Transformers for image recognition at scale. arXiv 2020 A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2010 | 555 | 2010 |
Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 515 | 2006 |
Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 515 | 2006 |
Monte-Carlo tree search and rapid action value estimation in computer Go S Gelly, D Silver Artificial Intelligence 175 (11), 1856-1875, 2011 | 433 | 2011 |
On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 336 | 2019 |
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 | 311 | 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 | 291 | 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 | 283 | 2006 |
Exploration exploitation in go: UCT for Monte-Carlo go S Gelly, Y Wang NIPS: Neural Information Processing Systems Conference On-line trading of …, 2006 | 283 | 2006 |
Adagan: Boosting generative models IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf Advances in neural information processing systems 30, 2017 | 242 | 2017 |
Episodic curiosity through reachability N Savinov, A Raichuk, R Marinier, D Vincent, M Pollefeys, T Lillicrap, ... arXiv preprint arXiv:1810.02274, 2018 | 220 | 2018 |
Achieving master level play in 9 x 9 computer go. S Gelly, D Silver AAAI 8, 1537-1540, 2008 | 167 | 2008 |
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 | 165 | 2018 |