| 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 | 1265 | 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 | 504 | 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 | 409 | 2020 |
| 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 | 355 | 2019 |
| 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 | 241 | 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 | 213 | 2019 |
| What matters for on-policy deep actor-critic methods? a large-scale study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... International conference on learning representations, 2021 | 193 | 2021 |
| The gan landscape: Losses, architectures, regularization, and normalization K Kurach, M Lucic, X Zhai, M Michalski, S Gelly | 163 | 2018 |
| Seed rl: Scalable and efficient deep-rl with accelerated central inference L Espeholt, R Marinier, P Stanczyk, K Wang, M Michalski arXiv preprint arXiv:1910.06591, 2019 | 149 | 2019 |
| FVD: A new metric for video generation T Unterthiner, S van Steenkiste, K Kurach, R Marinier, M Michalski, ... | 126 | 2019 |
| Are GANs created equal M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet A large-scale study. arXiv e-prints 2 (4), 2017 | 81 | 2017 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 77 | 2019 |
| What matters in on-policy reinforcement learning M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ... A large-scale empirical study. CoRR, abs/2006.05990 3, 2020 | 36 | 2020 |
| Failover operation on a replicated distributed database system while maintaining access invariance JCW Visser, M Michalski, R Blum, SV Gheorghita, JM Andersen US Patent 8,856,583, 2014 | 27 | 2014 |
| Assessment of changes in blood lactate levels in children and adolescents with type 1 diabetes during a football tournament (GoalDiab Study) J Flotyńska, A Gawrecki, A Araszkiewicz, M Parchimowicz, M Michalski, ... Pediatric Endocrinology Diabetes and Metabolism 27 (4), 237-244, 2021 | 3 | 2021 |
| SIO .NET Plug&Play Contest System M Michalski, M Kosieradzki, W Rygielski, P Stańczyk, K Ciebiera, K Diks Perspectives on Computer Science Competitions for (High School) Students, 2005 | 3 | 2005 |
| Reinforcement learning with centralized inference and training L Espeholt, K Wang, MM Michalski, PM Stanczyk, R Marinier US Patent App. 17/764,066, 2022 | 1 | 2022 |
| Assessment of factors influencing changes in blood lactate levels in children and adolescents with type 1 diabetes during a football tournament (GoalDiab Study) J Flotynska, A Gawrecki, A Araszkiewicz, M Parchimowicz, M Michalski, ... DIABETOLOGIA 64 (SUPPL 1), 186-187, 2021 | | 2021 |
| MemGEN: Memory is All You Need S Gelly, K Kurach, M Michalski, X Zhai arXiv preprint arXiv:1803.11203, 2018 | | 2018 |
