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Stochastic backpropagation and approximate inference in deep generative models DJ Rezende, S Mohamed, D Wierstra arXiv preprint arXiv:1401.4082, 2014 | 2083 | 2014 |
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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 | 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 |
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Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra arXiv preprint arXiv:1310.8499, 2013 | 166 | 2013 |
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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 |
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