| 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 | 1260 | 2020 |
| Combining online and offline knowledge in UCT S Gelly, D Silver Proceedings of the 24th international conference on Machine learning, 273-280, 2007 | 706 | 2007 |
| Wasserstein auto-encoders I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf arXiv preprint arXiv:1711.01558, 2017 | 681 | 2017 |
| Are gans created equal? a large-scale study M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet arXiv preprint arXiv:1711.10337, 2017 | 676 | 2017 |
| 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 | 560 | 2019 |
| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 501 | 2006 |
| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 501 | 2006 |
| Monte-Carlo tree search and rapid action value estimation in computer Go S Gelly, D Silver Artificial Intelligence 175 (11), 1856-1875, 2011 | 382 | 2011 |
| Exploration exploitation in go: UCT for Monte-Carlo go S Gelly, Y Wang NIPS: Neural Information Processing Systems Conference On-line trading of …, 2006 | 279 | 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 | 279 | 2006 |
| 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 | 266 | 2019 |
| 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 | 265 | 2012 |
| 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 | 244 | 2020 |
| Adagan: Boosting generative models I Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf arXiv preprint arXiv:1701.02386, 2017 | 192 | 2017 |
| Assessing generative models via precision and recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly arXiv preprint arXiv:1806.00035, 2018 | 190 | 2018 |
| On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 176 | 2019 |
| Achieving master level play in 9 x 9 computer go. S Gelly, D Silver AAAI 8, 1537-1540, 2008 | 163 | 2008 |
| Modifications of UCT and sequence-like simulations for Monte-Carlo Go Y Wang, S Gelly 2007 IEEE Symposium on Computational Intelligence and Games, 175-182, 2007 | 163 | 2007 |
| Episodic curiosity through reachability N Savinov, A Raichuk, R Marinier, D Vincent, M Pollefeys, T Lillicrap, ... arXiv preprint arXiv:1810.02274, 2018 | 137 | 2018 |
| The gan landscape: Losses, architectures, regularization, and normalization K Kurach, M Lucic, X Zhai, M Michalski, S Gelly | 119 | 2018 |
