David Pfau - RankMe

32 papers A* 5A 1Journal 19Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Generalized Bias-Variance Decomposition for Bregman Divergences. CoRR David Pfau
2025	A*	`conf`	Wasserstein Policy Optimization. ICML David Pfau, Ian Davies, Diana L. Borsa, João Guilherme Madeira Araújo, Brendan D. Tracey, Hado van Hasselt
2025	J	`jnl`	Wasserstein Policy Optimization. CoRR David Pfau, Ian Davies, Diana Borsa, João G. M. Araújo, Brendan D. Tracey, Hado van Hasselt
2023	A*	`conf`	A Self-Attention Ansatz for Ab-initio Quantum Chemistry. ICLR Ingrid von Glehn, James S. Spencer, David Pfau
2023	J	`jnl`	Natural Quantum Monte Carlo Computation of Excited States. CoRR David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, James S. Spencer
2023	J	`jnl`	Neural Wave Functions for Superfluids. CoRR Wan Tong Lou, Halvard Sutterud, Gino Cassella, W. Matthew C. Foulkes, Johannes Knolle, David Pfau, James S. Spencer
2022	J	`jnl`	A Self-Attention Ansatz for Ab-initio Quantum Chemistry. CoRR Ingrid von Glehn, James S. Spencer, David Pfau
2022	J	`jnl`	Ab-initio quantum chemistry with neural-network wavefunctions. CoRR Jan Hermann, James S. Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
2022	J	`jnl`	Discovering Quantum Phase Transitions with Fermionic Neural Networks. CoRR Gino Cassella, Halvard Sutterud, Sam Azadi, N. D. Drummond, David Pfau, James S. Spencer, W. Matthew C. Foulkes
2022	J	`jnl`	Magnetic control of tokamak plasmas through deep reinforcement learning. Nat. 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
2022	A*	`conf`	Making Sense of Raw Input (Extended Abstract). IJCAI Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot
2020	J	`jnl`	Better, Faster Fermionic Neural Networks. CoRR James S. Spencer, David Pfau, Aleksandar Botev, W. Matthew C. Foulkes
2020	A*	`conf`	Disentangling by Subspace Diffusion. NeurIPS David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
2020	J	`jnl`	Disentangling by Subspace Diffusion. CoRR David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
2020	J	`jnl`	Integrable Nonparametric Flows. CoRR David Pfau, Danilo J. Rezende
2019	J	`jnl`	Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. CoRR David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. Matthew C. Foulkes
2019	—	`conf`	Spectral Inference Networks: Unifying Deep and Spectral Learning. ICLR (Poster) David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld
2018	—	`conf`	Minimally Redundant Laplacian Eigenmaps. ICLR (Workshop) David Pfau, Christopher P. Burgess
2018	J	`jnl`	Spectral Inference Networks: Unifying Spectral Methods With Deep Learning. CoRR David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld
2018	J	`jnl`	Towards a Definition of Disentangled Representations. CoRR Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner
2017	—	`conf`	Unrolled Generative Adversarial Networks. ICLR (Poster) Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
2016	J	`jnl`	Connecting Generative Adversarial Networks and Actor-Critic Methods. CoRR David Pfau, Oriol Vinyals
2016	A	`conf`	Convolution by Evolution: Differentiable Pattern Producing Networks. GECCO Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
2016	J	`jnl`	Convolution by Evolution: Differentiable Pattern Producing Networks. CoRR Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
2016	—	`conf`	Learning to learn by gradient descent by gradient descent. NIPS Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
2016	J	`jnl`	Learning to learn by gradient descent by gradient descent. CoRR Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
2016	J	`jnl`	Unrolled Generative Adversarial Networks. CoRR Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
2015	J	`jnl`	Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. Finale Doshi-Velez, David Pfau, Frank D. Wood, Nicholas Roy
2013	—	`conf`	Robust learning of low-dimensional dynamics from large neural ensembles. NIPS David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
2012	—	`conf`	Decoding arm and hand movements across layers of the macaque frontal cortices. EMBC Yan Tat Wong, Mariana Vigeral, David Putrino, David Pfau, Josh Merel, Liam Paninski, Bijan Pesaran
2010	A*	`conf`	Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. ICML Nicholas Bartlett, David Pfau, Frank D. Wood
2010	—	`conf`	Probabilistic Deterministic Infinite Automata. NIPS David Pfau, Nicholas Bartlett, Frank D. Wood