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Integrable Nonparametric Flows

Dec 03, 2020
David Pfau, Danilo Rezende

* Accepted to 3rd NeurIPS Workshop on Machine Learning and Physical Sciences 

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Better, Faster Fermionic Neural Networks

Nov 13, 2020
James S. Spencer, David Pfau, Aleksandar Botev, W. M. C. Foulkes

* To appear at the 3rd NeurIPS Workshop on Machine Learning and Physical Science 

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Disentangling by Subspace Diffusion

Jun 23, 2020
David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

* 21 pages, 13 figures 

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Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

Sep 05, 2019
David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. M. C. Foulkes

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Towards a Definition of Disentangled Representations

Dec 05, 2018
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, Alexander Lerchner

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Spectral Inference Networks: Unifying Spectral Methods With Deep Learning

Jun 06, 2018
David Pfau, Stig Petersen, Ashish Agarwal, David Barrett, Kim Stachenfeld

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Unrolled Generative Adversarial Networks

May 12, 2017
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein

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Connecting Generative Adversarial Networks and Actor-Critic Methods

Jan 18, 2017
David Pfau, Oriol Vinyals

* Added comments on inverse reinforcement learning 

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Learning to learn by gradient descent by gradient descent

Nov 30, 2016
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas

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Convolution by Evolution: Differentiable Pattern Producing Networks

Jun 08, 2016
Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra

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