David Pfau
David Pfau
Staff Research Scientist at Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Unrolled generative adversarial networks
L Metz, B Poole, D Pfau, J Sohl-Dickstein
arXiv preprint arXiv:1611.02163, 2016
Learning to learn by gradient descent by gradient descent
M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ...
Advances in neural information processing systems 29, 2016
Simultaneous denoising, deconvolution, and demixing of calcium imaging data
EA Pnevmatikakis, D Soudry, Y Gao, TA Machado, J Merel, D Pfau, ...
Neuron 89 (2), 285-299, 2016
Magnetic control of tokamak plasmas through deep reinforcement learning
J Degrave, F Felici, J Buchli, M Neunert, B Tracey, F Carpanese, T Ewalds, ...
Nature 602 (7897), 414-419, 2022
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
D Pfau, JS Spencer, AGG Matthews, WMC Foulkes
arXiv preprint arXiv:1909.02487, 2019
Towards a definition of disentangled representations
I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
Pushing the frontiers of density functionals by solving the fractional electron problem
J Kirkpatrick, B McMorrow, DHP Turban, AL Gaunt, JS Spencer, ...
Science 374 (6573), 1385-1389, 2021
Connecting generative adversarial networks and actor-critic methods
D Pfau, O Vinyals
arXiv preprint arXiv:1610.01945, 2016
Convolution by evolution: Differentiable pattern producing networks
C Fernando, D Banarse, M Reynolds, F Besse, D Pfau, M Jaderberg, ...
Proceedings of the Genetic and Evolutionary Computation Conference 2016, 109-116, 2016
Bayesian nonparametric methods for partially-observable reinforcement learning
F Doshi-Velez, D Pfau, F Wood, N Roy
IEEE transactions on pattern analysis and machine intelligence 37 (2), 394-407, 2013
Robust learning of low-dimensional dynamics from large neural ensembles
D Pfau, EA Pnevmatikakis, L Paninski
Advances in neural information processing systems 26, 2013
Discovering quantum phase transitions with fermionic neural networks
G Cassella, H Sutterud, S Azadi, ND Drummond, D Pfau, JS Spencer, ...
Physical Review Letters 130 (3), 036401, 2023
A structured matrix factorization framework for large scale calcium imaging data analysis
EA Pnevmatikakis, Y Gao, D Soudry, D Pfau, C Lacefield, K Poskanzer, ...
arXiv preprint arXiv:1409.2903, 2014
Better, faster fermionic neural networks
JS Spencer, D Pfau, A Botev, WMC Foulkes
arXiv preprint arXiv:2011.07125, 2020
Spectral inference networks: Unifying deep and spectral learning
D Pfau, S Petersen, A Agarwal, DGT Barrett, KL Stachenfeld
arXiv preprint arXiv:1806.02215, 2018
Ab initio quantum chemistry with neural-network wavefunctions
J Hermann, J Spencer, K Choo, A Mezzacapo, WMC Foulkes, D Pfau, ...
Nature Reviews Chemistry 7 (10), 692-709, 2023
A self-attention ansatz for ab-initio quantum chemistry
I von Glehn, JS Spencer, D Pfau
arXiv preprint arXiv:2211.13672, 2022
Making sense of raw input
R Evans, M Bošnjak, L Buesing, K Ellis, D Pfau, P Kohli, M Sergot
Artificial Intelligence 299, 103521, 2021
Probabilistic deterministic infinite automata
D Pfau, N Bartlett, F Wood
Advances in neural information processing systems 23, 2010
Disentangling by subspace diffusion
D Pfau, I Higgins, A Botev, S Racaničre
Advances in Neural Information Processing Systems 33, 17403-17415, 2020
The system can't perform the operation now. Try again later.
Articles 1–20