Daan Wierstra
Daan Wierstra
Principal Scientist, DeepMind
在 google.com 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
167692015
Playing atari with deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ...
arXiv preprint arXiv:1312.5602, 2013
72232013
Continuous control with deep reinforcement learning
TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ...
arXiv preprint arXiv:1509.02971, 2015
70032015
Stochastic backpropagation and approximate inference in deep generative models
DJ Rezende, S Mohamed, D Wierstra
International conference on machine learning, 1278-1286, 2014
38632014
Matching networks for one shot learning
O Vinyals, C Blundell, T Lillicrap, D Wierstra
Advances in neural information processing systems 29, 3630-3638, 2016
33922016
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
International conference on machine learning, 387-395, 2014
24402014
Weight uncertainty in neural network
C Blundell, J Cornebise, K Kavukcuoglu, D Wierstra
International Conference on Machine Learning, 1613-1622, 2015
18082015
Draw: A recurrent neural network for image generation
K Gregor, I Danihelka, A Graves, D Rezende, D Wierstra
International Conference on Machine Learning, 1462-1471, 2015
17842015
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
14752018
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
12092016
Natural evolution strategies
D Wierstra, T Schaul, T Glasmachers, Y Sun, J Peters, J Schmidhuber
The Journal of Machine Learning Research 15 (1), 949-980, 2014
6182014
Pathnet: Evolution channels gradient descent in super neural networks
C Fernando, D Banarse, C Blundell, Y Zwols, D Ha, AA Rusu, A Pritzel, ...
arXiv preprint arXiv:1701.08734, 2017
4792017
PyBrain
T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ...
Journal of Machine Learning Research 11 (ARTICLE), 743-746, 2010
4402010
One-shot learning with memory-augmented neural networks
A Santoro, S Bartunov, M Botvinick, D Wierstra, T Lillicrap
arXiv preprint arXiv:1605.06065, 2016
4242016
Neural scene representation and rendering
SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ...
Science 360 (6394), 1204-1210, 2018
3982018
Training recurrent networks by evolino
J Schmidhuber, D Wierstra, M Gagliolo, F Gomez
Neural computation 19 (3), 757-779, 2007
2842007
Imagination-augmented agents for deep reinforcement learning
S Racanière, T Weber, DP Reichert, L Buesing, A Guez, D Rezende, ...
Proceedings of the 31st International Conference on Neural Information …, 2017
2612017
Deep autoregressive networks
K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra
International Conference on Machine Learning, 1242-1250, 2014
2452014
One-shot generalization in deep generative models
D Rezende, I Danihelka, K Gregor, D Wierstra
International Conference on Machine Learning, 1521-1529, 2016
2332016
A system for robotic heart surgery that learns to tie knots using recurrent neural networks
H Mayer, F Gomez, D Wierstra, I Nagy, A Knoll, J Schmidhuber
Advanced Robotics 22 (13-14), 1521-1537, 2008
2292008
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