David Pfau
Cited by
Cited by
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, 3981-3989, 2016
Unrolled generative adversarial networks
L Metz, B Poole, D Pfau, J Sohl-Dickstein
arXiv preprint arXiv:1611.02163, 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
Towards a definition of disentangled representations
I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
Connecting generative adversarial networks and actor-critic methods
D Pfau, O Vinyals
arXiv preprint arXiv:1610.01945, 2016
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
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
Robust learning of low-dimensional dynamics from large neural ensembles.
D Pfau, EA Pnevmatikakis, L Paninski
NIPS 2013, 2391-2399, 2013
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
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
Probabilistic Deterministic Infinite Automata.
D Pfau, N Bartlett, FD Wood
NIPS, 1930-1938, 2010
Spectral inference networks: Unifying deep and spectral learning
D Pfau, S Petersen, A Agarwal, DGT Barrett, KL Stachenfeld
arXiv preprint arXiv:1806.02215, 2018
Forgetting counts: Constant memory inference for a dependent hierarchical Pitman-Yor process
N Bartlett, D Pfau, FD Wood
ICML, 2010
Disentangling by subspace diffusion
D Pfau, I Higgins, A Botev, S Racaničre
arXiv preprint arXiv:2006.12982, 2020
Better, faster fermionic neural networks
JS Spencer, D Pfau, A Botev, WMC Foulkes
arXiv preprint arXiv:2011.07125, 2020
Dead leaves and the dirty ground: low-level image statistics in transmissive and occlusive imaging environments
J Zylberberg, D Pfau, MR DeWeese
Physical Review E 86 (6), 066112, 2012
Making sense of raw input
R Evans, M Bošnjak, L Buesing, K Ellis, D Pfau, P Kohli, M Sergot
Artificial Intelligence 299, 103521, 2021
A generalized bias-variance decomposition for bregman divergences
D Pfau
Unpublished Manuscript, 2013
Training machine learning models
MMR Denil, T Schaul, M Andrychowicz, JFG De Freitas, SG Colmenarejo, ...
US Patent App. 16/302,592, 2019
Decoding arm and hand movements across layers of the macaque frontal cortices
YT Wong, M Vigeral, D Putrino, D Pfau, J Merel, L Paninski, B Pesaran
2012 Annual International Conference of the IEEE Engineering in Medicine and …, 2012
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Articles 1–20