| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... nature 575 (7782), 350-354, 2019 | 5124* | 2019 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 3679 | 2021 |
| Openai baselines P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ... | 1920* | 2017 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1184 | 2023 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 891 | 2022 |
| Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation Y Wu, E Mansimov, RB Grosse, S Liao, J Ba Advances in Neural Information Processing Systems, 5283-5292, 2017 | 823 | 2017 |
| Solving quantitative reasoning problems with language models A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... Advances in Neural Information Processing Systems 35, 3843-3857, 2022 | 534 | 2022 |
| STaR: Bootstrapping reasoning with reasoning E Zelikman, Y Wu, ND Goodman arXiv preprint arXiv:2203.14465, 2022 | 373 | 2022 |
| Backpropagation through the void: Optimizing control variates for black-box gradient estimation W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud ICLR2018, 2017 | 324 | 2017 |
| Sticking the landing: Simple, lower-variance gradient estimators for variational inference G Roeder, Y Wu, DK Duvenaud Advances in Neural Information Processing Systems 30, 2017 | 281* | 2017 |
| On the quantitative analysis of decoder-based generative models Y Wu, Y Burda, R Salakhutdinov, R Grosse 5th International Conference on Learning Representations (ICLR 2017), 2016 | 271 | 2016 |
| Memorizing Transformers Y Wu, MN Rabe, DL Hutchins, C Szegedy International Conference on Learning Representations 2022, 2022 | 215 | 2022 |
| Architectural complexity measures of recurrent neural networks S Zhang, Y Wu, T Che, Z Lin, R Memisevic, RR Salakhutdinov, Y Bengio Advances in neural information processing systems 29, 2016 | 193 | 2016 |
| STDP-compatible approximation of backpropagation in an energy-based model Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu Neural computation 29 (3), 555-577, 2017 | 184* | 2017 |
| On multiplicative integration with recurrent neural networks Y Wu, S Zhang, Y Zhang, Y Bengio, RR Salakhutdinov Advances in neural information processing systems 29, 2016 | 184 | 2016 |
| Solving olympiad geometry without human demonstrations TH Trinh, Y Wu, QV Le, H He, T Luong Nature 625 (7995), 476-482, 2024 | 177 | 2024 |
| The Importance of Sampling in Meta-Reinforcement Learning B Stadie, G Yang, R Houthooft, P Chen, Y Duan, Y Wu, P Abbeel, ... Advances in Neural Information Processing Systems, 9299-9309, 2018 | 172* | 2018 |
| Exploring length generalization in large language models C Anil, Y Wu, A Andreassen, A Lewkowycz, V Misra, V Ramasesh, ... Advances in Neural Information Processing Systems 35, 38546-38556, 2022 | 149 | 2022 |
| Understanding Short-Horizon Bias in Stochastic Meta-Optimization Y Wu, M Ren, R Liao, RB Grosse Sixth International Conference on Learning Representations (ICLR 2018), 2018 | 140 | 2018 |
| Invariant Causal Representation Learning for Out-of-Distribution Generalization C Lu, Y Wu, JM Hernández-Lobato, B Schölkopf International Conference on Learning Representations, 2022 | 136* | 2022 |
Roger GrosseAssociate Professor, University of TorontoPatvirtintas el. paštas cs.toronto.edu
