David Pfau

According to our database, David Pfau authored at least 24 papers between 2010 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Natural Quantum Monte Carlo Computation of Excited States.

David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, James S. Spencer

CoRR, 2023

Neural Wave Functions for Superfluids.

Wan Tong Lou, Halvard Sutterud, Gino Cassella, W. Matthew C. Foulkes, Johannes Knolle, David Pfau, James S. Spencer

CoRR, 2023

A Self-Attention Ansatz for Ab-initio Quantum Chemistry.

Ingrid von Glehn, James S. Spencer, David Pfau

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Magnetic control of tokamak plasmas through deep reinforcement learning.

Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan D. Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, Martin A. Riedmiller

Nat., 2022

Ab-initio quantum chemistry with neural-network wavefunctions.

Jan Hermann, James S. Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé

CoRR, 2022

Discovering Quantum Phase Transitions with Fermionic Neural Networks.

Gino Cassella, Halvard Sutterud, Sam Azadi, N. D. Drummond, David Pfau, James S. Spencer, W. Matthew C. Foulkes

CoRR, 2022

Making Sense of Raw Input (Extended Abstract).

Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2020

Integrable Nonparametric Flows.

David Pfau, Danilo J. Rezende

CoRR, 2020

Better, Faster Fermionic Neural Networks.

James S. Spencer, David Pfau, Aleksandar Botev, W. Matthew C. Foulkes

CoRR, 2020

Disentangling by Subspace Diffusion.

David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

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

David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. Matthew C. Foulkes

CoRR, 2019

Spectral Inference Networks: Unifying Deep and Spectral Learning.

David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Towards a Definition of Disentangled Representations.

Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner

CoRR, 2018

Spectral Inference Networks: Unifying Spectral Methods With Deep Learning.

David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld

CoRR, 2018

Minimally Redundant Laplacian Eigenmaps.

David Pfau, Christopher P. Burgess

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Unrolled Generative Adversarial Networks.

Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Connecting Generative Adversarial Networks and Actor-Critic Methods.

David Pfau, Oriol Vinyals

CoRR, 2016

Learning to learn by gradient descent by gradient descent.

Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Convolution by Evolution: Differentiable Pattern Producing Networks.

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

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

2015

Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning.

Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy

IEEE Trans. Pattern Anal. Mach. Intell., 2015

2013

Robust learning of low-dimensional dynamics from large neural ensembles.

David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Decoding arm and hand movements across layers of the macaque frontal cortices.

Yan Tat Wong, Mariana Vigeral, David Putrino, David Pfau, Josh Merel, Liam Paninski, Bijan Pesaran

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

2010

Probabilistic Deterministic Infinite Automata.

David Pfau, Nicholas Bartlett, Frank D. Wood

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process.

Nicholas Bartlett, David Pfau, Frank D. Wood

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010