David Pfau

Publication Activity (10 Years)

Years Active: 2010-2023
Publications (10 Years): 25

Top Topics

Top Venues

IEEE Trans. Pattern Anal. Mach. Intell.

Publications

Wan Tong Lou, Halvard Sutterud, Gino Cassella, W. Matthew C. Foulkes, Johannes Knolle, David Pfau, James S. Spencer
Neural Wave Functions for Superfluids. CoRR (2023)
Ingrid von Glehn, James S. Spencer, David Pfau
A Self-Attention Ansatz for Ab-initio Quantum Chemistry. ICLR (2023)
David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, James S. Spencer
Natural Quantum Monte Carlo Computation of Excited States. CoRR (2023)
Ingrid von Glehn, James S. Spencer, David Pfau
A Self-Attention Ansatz for Ab-initio Quantum Chemistry. CoRR (2022)
G. Cassella, H. Sutterud, S. Azadi, N. D. Drummond, David Pfau, J. S. Spencer, W. Matthew C. Foulkes
Discovering Quantum Phase Transitions with Fermionic Neural Networks. CoRR (2022)
Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
Ab-initio quantum chemistry with neural-network wavefunctions. CoRR (2022)
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
Magnetic control of tokamak plasmas through deep reinforcement learning. Nat. 602 (7897) (2022)
Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot
Making Sense of Raw Input (Extended Abstract). IJCAI (2022)
David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
Disentangling by Subspace Diffusion. CoRR (2020)
David Pfau, Danilo J. Rezende
Integrable Nonparametric Flows. CoRR (2020)
James S. Spencer, David Pfau, Aleksandar Botev, W. Matthew C. Foulkes
Better, Faster Fermionic Neural Networks. CoRR (2020)
David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
Disentangling by Subspace Diffusion. NeurIPS (2020)
David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld
Spectral Inference Networks: Unifying Deep and Spectral Learning. ICLR (Poster) (2019)
David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. Matthew C. Foulkes
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. CoRR (2019)
Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner
Towards a Definition of Disentangled Representations. CoRR (2018)
David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning. CoRR (2018)
David Pfau, Christopher P. Burgess
Minimally Redundant Laplacian Eigenmaps. ICLR (Workshop) (2018)
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
Unrolled Generative Adversarial Networks. ICLR (Poster) (2017)
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
Unrolled Generative Adversarial Networks. CoRR (2016)
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
Learning to learn by gradient descent by gradient descent. CoRR (2016)
Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
Convolution by Evolution: Differentiable Pattern Producing Networks. GECCO (2016)
David Pfau, Oriol Vinyals
Connecting Generative Adversarial Networks and Actor-Critic Methods. CoRR (2016)
Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
Convolution by Evolution: Differentiable Pattern Producing Networks. CoRR (2016)
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
Learning to learn by gradient descent by gradient descent. NIPS (2016)
Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
Robust learning of low-dimensional dynamics from large neural ensembles. NIPS (2013)
Yan Tat Wong, Mariana Vigeral, David Putrino, David Pfau, Josh Merel, Liam Paninski, Bijan Pesaran
Decoding arm and hand movements across layers of the macaque frontal cortices. EMBC (2012)
David Pfau, Nicholas Bartlett, Frank D. Wood
Probabilistic Deterministic Infinite Automata. NIPS (2010)
Nicholas Bartlett, David Pfau, Frank D. Wood
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. ICML (2010)