| 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 | 16666 | 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 | 1299 | 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 | 1146 | 2019 |
| Wasserstein auto-encoders I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf arXiv preprint arXiv:1711.01558, 2017 | 1037 | 2017 |
| 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 | 991 | 2018 |
| 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 | 881 | 2010 |
| 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 | 842 | 2020 |
| Combining online and offline knowledge in UCT S Gelly, D Silver Proceedings of the 24th international conference on Machine learning, 273-280, 2007 | 749 | 2007 |
| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 522 | 2006 |
| Modification of UCT with patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006 | 522 | 2006 |
| Monte-Carlo tree search and rapid action value estimation in computer Go S Gelly, D Silver Artificial Intelligence 175 (11), 1856-1875, 2011 | 454 | 2011 |
| On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 386 | 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 | 369 | 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 | 296 | 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 | 290 | 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 | 290 | 2006 |
| Adagan: Boosting generative models IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf Advances in neural information processing systems 30, 2017 | 255 | 2017 |
| Episodic curiosity through reachability N Savinov, A Raichuk, R Marinier, D Vincent, M Pollefeys, T Lillicrap, ... arXiv preprint arXiv:1810.02274, 2018 | 247 | 2018 |
| 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 | 202 | 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 | 195 | 2020 |
