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Towards Biologically Plausible Convolutional Networks

Jun 22, 2021
Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham

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Training Generative Adversarial Networks by Solving Ordinary Differential Equations

Oct 28, 2020
Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli

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Compressive Transformers for Long-Range Sequence Modelling

Nov 13, 2019
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap

* 19 pages, 6 figures, 10 tables 

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Automated curricula through setter-solver interactions

Sep 27, 2019
Sebastien Racaniere, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap

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What does it mean to understand a neural network?

Jul 15, 2019
Timothy P. Lillicrap, Konrad P. Kording

* 9 pages, 2 figures 

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Experience Replay for Continual Learning

Nov 28, 2018
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne

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Learning to Learn without Gradient Descent by Gradient Descent

Jun 12, 2017
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas

* Accepted by ICML 2017. Previous version "Learning to Learn for Global Optimization of Black Box Functions" was published in the Deep Reinforcement Learning Workshop, NIPS 2016 

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Asynchronous Methods for Deep Reinforcement Learning

Jun 16, 2016
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

* ICML 2016 

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Continuous control with deep reinforcement learning

Feb 29, 2016
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra

* 10 pages + supplementary 

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Random feedback weights support learning in deep neural networks

Nov 02, 2014
Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, Colin J. Akerman

* 14 pages, 5 figures in main text; 13 pages appendix 

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