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David Silver
David Silver
DeepMind, UCL
Geverifieerd e-mailadres voor google.com - Homepage
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Human-level control through deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ...
nature 518 (7540), 529-533, 2015
328552015
Highly accurate protein structure prediction with AlphaFold
J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ...
nature 596 (7873), 583-589, 2021
294392021
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
204502016
Continuous control with deep reinforcement learning
TP Lillicrap
arXiv preprint arXiv:1509.02971, 2015
177912015
Playing atari with deep reinforcement learning
V Mnih
arXiv preprint arXiv:1312.5602, 2013
163672013
Asynchronous Methods for Deep Reinforcement Learning
V Mnih
arXiv preprint arXiv:1602.01783, 2016
120572016
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
116602017
Deep reinforcement learning with double q-learning
H Van Hasselt, A Guez, D Silver
Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016
100182016
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
International conference on machine learning, 387-395, 2014
55032014
Prioritized Experience Replay
T Schaul
arXiv preprint arXiv:1511.05952, 2015
52312015
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
49342018
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
48942019
Improved protein structure prediction using potentials from deep learning
AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, ...
Nature 577 (7792), 706-710, 2020
34072020
Rainbow: Combining improvements in deep reinforcement learning
M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ...
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
28432018
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
26182020
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
24242017
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
21442023
Monte-Carlo planning in large POMDPs
D Silver, J Veness
Advances in neural information processing systems 23, 2010
15162010
Reinforcement learning with unsupervised auxiliary tasks
M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ...
arXiv preprint arXiv:1611.05397, 2016
14492016
Universal value function approximators
T Schaul, D Horgan, K Gregor, D Silver
International conference on machine learning, 1312-1320, 2015
12772015
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Artikelen 1–20