| Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... nature 529 (7587), 484-489, 2016 | 20388 | 2016 |
| Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... ICLR 2016; arXiv preprint arXiv:1509.02971, 2015 | 17687 | 2015 |
| Asynchronous methods for deep reinforcement learning V Mnih, AP Badia, M Mirza, A Graves, TP Lillicrap, T Harley, D Silver, ... arXiv:1602.01783, 2016 | 12005 | 2016 |
| Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, ... nature 550 (7676), 354-359, 2017 | 11601 | 2017 |
| Matching networks for one shot learning O Vinyals, C Blundell, T Lillicrap, K Kavukcuoglu, D Wierstra arXiv preprint arXiv:1606.04080, 2016 | 8698 | 2016 |
| A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... Science 362 (6419), 1140-1144, 2018 | 4909 | 2018 |
| 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 | 4864 | 2019 |
| Meta-learning with memory-augmented neural networks A Santoro, S Bartunov, M Botvinick, D Wierstra, T Lillicrap International conference on machine learning, 1842-1850, 2016 | 3167 | 2016 |
| Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, ... Nature 588 (7839), 604-609, 2020 | 2611 | 2020 |
| Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... arXiv preprint arXiv:1712.01815, 2017 | 2509 | 2017 |
| Deep reinforcement learning for robotic manipulation S Gu, E Holly, T Lillicrap, S Levine arXiv:1610.00633, 2016 | 2108* | 2016 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2070 | 2023 |
| A simple neural network module for relational reasoning A Santoro, D Raposo, DG Barrett, M Malinowski, R Pascanu, P Battaglia, ... Advances in neural information processing systems 30, 2017 | 1932 | 2017 |
| Learning latent dynamics for planning from pixels D Hafner, T Lillicrap, I Fischer, R Villegas, D Ha, H Lee, J Davidson International conference on machine learning, 2555-2565, 2019 | 1638 | 2019 |
| Dream to control: Learning behaviors by latent imagination D Hafner, T Lillicrap, J Ba, M Norouzi arXiv preprint arXiv:1912.01603, 2019 | 1396 | 2019 |
| Continuous deep Q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine ICML2016; arXiv:1603.00748 [cs.LG], 2016 | 1326 | 2016 |
| Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, T Lillicrap, G Wayne Advances in neural information processing systems 32, 2019 | 1133 | 2019 |
| Starcraft ii: A new challenge for reinforcement learning O Vinyals, T Ewalds, S Bartunov, P Georgiev, AS Vezhnevets, M Yeo, ... arXiv preprint arXiv:1708.04782, 2017 | 1115 | 2017 |
| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 1029 | 2019 |
| Backpropagation and the brain TP Lillicrap, A Santoro, L Marris, CJ Akerman, G Hinton Nature Reviews Neuroscience 21 (6), 335-346, 2020 | 963 | 2020 |
David SilverDeepMind, UCLEmail verificata su google.com