| 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 | 32751 | 2015 |
| Continuous control with deep reinforcement learning TP Lillicrap arXiv preprint arXiv:1509.02971, 2015 | 17679 | 2015 |
| Playing atari with deep reinforcement learning V Mnih arXiv preprint arXiv:1312.5602, 2013 | 16307 | 2013 |
| Matching networks for one shot learning O Vinyals, C Blundell, T Lillicrap, D Wierstra Advances in neural information processing systems 29, 2016 | 8658 | 2016 |
| Stochastic backpropagation and approximate inference in deep generative models DJ Rezende, S Mohamed, D Wierstra International conference on machine learning, 1278-1286, 2014 | 6154 | 2014 |
| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 | 5480 | 2014 |
| Weight uncertainty in neural network C Blundell, J Cornebise, K Kavukcuoglu, D Wierstra International conference on machine learning, 1613-1622, 2015 | 4295 | 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 | 3907 | 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 | 3173 | 2016 |
| 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 | 2528 | 2015 |
| 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 | 1100 | 2014 |
| Playing atari with deep reinforcement learning. arXiv 2013 V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ... arXiv preprint arXiv:1312.5602, 2013 | 1061 | 2013 |
| 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 | 1008 | 2017 |
| Neural scene representation and rendering SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ... Science 360 (6394), 1204-1210, 2018 | 736 | 2018 |
| Continuous control with deep reinforcement learning. arXiv 2015 TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 1935 | 598 | 1935 |
| Imagination-augmented agents for deep reinforcement learning S Racanière, T Weber, D Reichert, L Buesing, A Guez, ... Advances in neural information processing systems 30, 2017 | 482 | 2017 |
| Variational intrinsic control K Gregor, DJ Rezende, D Wierstra arXiv preprint arXiv:1611.07507, 2016 | 473 | 2016 |
| PyBrain T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ... Journal of Machine Learning Research 11, 743-746, 2010 | 464 | 2010 |
| Neural episodic control A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ... International conference on machine learning, 2827-2836, 2017 | 429 | 2017 |
| Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra International Conference on Machine Learning, 1242-1250, 2014 | 359 | 2014 |
Tom SchaulSenior Staff Scientist, DeepMindVerifierad e-postadress på nyu.edu