| 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 | 13582 | 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 | 5670 | 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 | 5241 | 2015 |
| Stochastic backpropagation and approximate inference in deep generative models DJ Rezende, S Mohamed, D Wierstra International conference on machine learning, 1278-1286, 2014 | 3278 | 2014 |
| Matching networks for one shot learning O Vinyals, C Blundell, T Lillicrap, K Kavukcuoglu, D Wierstra arXiv preprint arXiv:1606.04080, 2016 | 2438 | 2016 |
| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 | 1900 | 2014 |
| 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 | 1590 | 2015 |
| Weight uncertainty in neural network C Blundell, J Cornebise, K Kavukcuoglu, D Wierstra International Conference on Machine Learning, 1613-1622, 2015 | 1352 | 2015 |
| 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 | 1054 | 2018 |
| 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 | 935 | 2016 |
| 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 | 426 | 2010 |
| 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 | 370 | 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 | 360 | 2016 |
| Natural evolution strategies D Wierstra, T Schaul, J Peters, J Schmidhuber 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on …, 2008 | 293 | 2008 |
| Neural scene representation and rendering SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ... Science 360 (6394), 1204-1210, 2018 | 286 | 2018 |
| 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 | 279 | 2014 |
| Training recurrent networks by evolino J Schmidhuber, D Wierstra, M Gagliolo, F Gomez Neural computation 19 (3), 757-779, 2007 | 272 | 2007 |
| Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra International Conference on Machine Learning, 1242-1250, 2014 | 226 | 2014 |
| One-shot generalization in deep generative models D Rezende, I Danihelka, K Gregor, D Wierstra International Conference on Machine Learning, 1521-1529, 2016 | 212 | 2016 |
| 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 | 205 | 2008 |
Tom SchaulSenior Staff Research Scientist, DeepMindVahvistettu sähköpostiosoite verkkotunnuksessa nyu.edu
