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| Stochastic backpropagation and approximate inference in deep generative models DJ Rezende, S Mohamed, D Wierstra International conference on machine learning, 1278-1286, 2014 | 3389 | 2014 |
| Matching networks for one shot learning O Vinyals, C Blundell, T Lillicrap, K Kavukcuoglu, D Wierstra arXiv preprint arXiv:1606.04080, 2016 | 2608 | 2016 |
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| 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 | 1129 | 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 | 978 | 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 | 431 | 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 | 392 | 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 | 371 | 2016 |
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| Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra International Conference on Machine Learning, 1242-1250, 2014 | 231 | 2014 |
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Tom SchaulSenior Staff Research Scientist, DeepMindVerified email at nyu.edu
