Подписаться
Timothy P. Lillicrap
Timothy P. Lillicrap
Director of Research, Google DeepMind
Подтвержден адрес электронной почты в домене google.com - Главная страница
Название
Процитировано
Процитировано
Год
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
210562016
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
184852015
Asynchronous methods for deep reinforcement learning
V Mnih, AP Badia, M Mirza, A Graves, TP Lillicrap, T Harley, D Silver, ...
arXiv:1602.01783, 2016
124332016
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
119882017
Matching networks for one shot learning
O Vinyals, C Blundell, T Lillicrap, K Kavukcuoglu, D Wierstra
arXiv preprint arXiv:1606.04080, 2016
89992016
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
51302018
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
50962019
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
32482016
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
27362020
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
26242017
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
25622023
Deep reinforcement learning for robotic manipulation
S Gu, E Holly, T Lillicrap, S Levine
arXiv:1610.00633, 2016
2169*2016
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
19692017
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
17142019
Dream to control: Learning behaviors by latent imagination
D Hafner, T Lillicrap, J Ba, M Norouzi
arXiv preprint arXiv:1912.01603, 2019
14772019
Continuous deep Q-learning with model-based acceleration
S Gu, T Lillicrap, I Sutskever, S Levine
ICML2016; arXiv:1603.00748 [cs.LG], 2016
13552016
Experience replay for continual learning
D Rolnick, A Ahuja, J Schwarz, T Lillicrap, G Wayne
Advances in neural information processing systems 32, 2019
12232019
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
11502017
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
10872019
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
10392024
В данный момент система не может выполнить эту операцию. Повторите попытку позднее.
Статьи 1–20