| 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 | 2562 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 1031 | 2024 |
| Modelling the N400 brain potential as change in a probabilistic representation of meaning M Rabovsky, SS Hansen, JL McClelland Nature Human Behaviour 2 (9), 693-705, 2018 | 275 | 2018 |
| Unsupervised control through non-parametric discriminative rewards D Warde-Farley, T Van de Wiele, T Kulkarni, C Ionescu, S Hansen, ... arXiv preprint arXiv:1811.11359, 2018 | 196 | 2018 |
| Fast task inference with variational intrinsic successor features S Hansen, W Dabney, A Barreto, T Van de Wiele, D Warde-Farley, V Mnih arXiv preprint arXiv:1906.05030, 2019 | 189 | 2019 |
| In-context reinforcement learning with algorithm distillation M Laskin, L Wang, J Oh, E Parisotto, S Spencer, R Steigerwald, ... arXiv preprint arXiv:2210.14215, 2022 | 118 | 2022 |
| Generalization of reinforcement learners with working and episodic memory M Fortunato, M Tan, R Faulkner, S Hansen, A Puigdomènech Badia, ... Advances in neural information processing systems 32, 2019 | 80 | 2019 |
| Learning more skills through optimistic exploration DJ Strouse, K Baumli, D Warde-Farley, V Mnih, S Hansen arXiv preprint arXiv:2107.14226, 2021 | 52 | 2021 |
| Relative variational intrinsic control K Baumli, D Warde-Farley, S Hansen, V Mnih Proceedings of the AAAI conference on artificial intelligence 35 (8), 6732-6740, 2021 | 50 | 2021 |
| Fast deep reinforcement learning using online adjustments from the past S Hansen, A Pritzel, P Sprechmann, A Barreto, C Blundell Advances in Neural Information Processing Systems 31, 2018 | 48 | 2018 |
| Beyond fine-tuning: Transferring behavior in reinforcement learning V Campos, P Sprechmann, S Hansen, A Barreto, S Kapturowski, ... arXiv preprint arXiv:2102.13515, 2021 | 30 | 2021 |
| Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behaviour, 2 (9), 693–705 M Rabovsky, SS Hansen, JL McClelland Retrieved 2019-12-04, from https://www. nature. com/articles/s41562-018-0406 …, 2018 | 29 | 2018 |
| A parallel-distributed processing approach to mathematical cognition JL McClelland, K Mickey, S Hansen, A Yuan, Q Lu Manuscript, Stanford University, 263, 2016 | 18 | 2016 |
| N400 amplitudes reflect change in a probabilistic representation of meaning: Evidence from a connectionist model. M Rabovsky, SS Hansen, JL McClelland CogSci, 2016 | 12 | 2016 |
| Coverage as a principle for discovering transferable behavior in reinforcement learning V Campos, P Sprechmann, SS Hansen, A Barreto, C Blundell, A Vitvitskyi, ... | 9 | 2021 |
| Building on prior knowledge without building it in SS Hansen, A Lampinen, G Suri, JL McClelland Behavioral and Brain Sciences 40, e268, 2017 | 9 | 2017 |
| Expressing non-Markov reward to a Markov agent D Abel, A Barreto, M Bowling, W Dabney, S Hansen, A Harutyunyan, ... Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2022 | 8 | 2022 |
| Entropic desired dynamics for intrinsic control S Hansen, G Desjardins, K Baumli, D Warde-Farley, N Heess, S Osindero, ... Advances in Neural Information Processing Systems 34, 11436-11448, 2021 | 7 | 2021 |
| I like coffee with cream and dog? Change in an implicit probabilistic representation captures meaning processing in the brain M Rabovsky, SS Hansen, JL McClelland bioRxiv, 138149, 2017 | 5 | 2017 |
| Wasserstein distance maximizing intrinsic control I Durugkar, S Hansen, S Spencer, V Mnih arXiv preprint arXiv:2110.15331, 2021 | 4 | 2021 |
Volodymyr MnihDeepMindAdresse e-mail validée de cs.toronto.edu