| Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... Nature 518 (7540), 529, 2015 | 7859 | 2015 |
| Playing atari with deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ... arXiv preprint arXiv:1312.5602, 2013 | 3281 | 2013 |
| 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 | 2599 | 2015 |
| Stochastic backpropagation and approximate inference in deep generative models DJ Rezende, S Mohamed, D Wierstra arXiv preprint arXiv:1401.4082, 2014 | 2083 | 2014 |
| Draw: A recurrent neural network for image generation K Gregor, I Danihelka, A Graves, DJ Rezende, D Wierstra arXiv preprint arXiv:1502.04623, 2015 | 1179 | 2015 |
| Matching networks for one shot learning O Vinyals, C Blundell, T Lillicrap, D Wierstra Advances in neural information processing systems, 3630-3638, 2016 | 1039 | 2016 |
| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller | 1006 | 2014 |
| Weight uncertainty in neural networks C Blundell, J Cornebise, K Kavukcuoglu, D Wierstra arXiv preprint arXiv:1505.05424, 2015 | 677 | 2015 |
| 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 | 431 | 2016 |
| PyBrain T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T RĆ¼ckstieĆ, ... Journal of Machine Learning Research 11 (Feb), 743-746, 2010 | 383 | 2010 |
| 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 | 367 | 2018 |
| Training recurrent networks by evolino J Schmidhuber, D Wierstra, M Gagliolo, F Gomez Neural computation 19 (3), 757-779, 2007 | 239 | 2007 |
| 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 | 232 | 2017 |
| One-shot learning with memory-augmented neural networks A Santoro, S Bartunov, M Botvinick, D Wierstra, T Lillicrap arXiv preprint arXiv:1605.06065, 2016 | 232 | 2016 |
| Natural evolution strategies D Wierstra, T Schaul, J Peters, J Schmidhuber 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on …, 2008 | 217 | 2008 |
| Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra arXiv preprint arXiv:1310.8499, 2013 | 166 | 2013 |
| 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 | 162 | 2014 |
| 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 | 157 | 2008 |
| One-shot generalization in deep generative models DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra arXiv preprint arXiv:1603.05106, 2016 | 152 | 2016 |
| Neural scene representation and rendering SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ... Science 360 (6394), 1204-1210, 2018 | 150 | 2018 |
Tom SchaulDeepMindVerified email at nyu.edu
