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David Pfau
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- affiliation: DeepMind, London, UK
- affiliation: Columbia University, Center for Theoretical Neuroscience
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2020 – today
- 2023
- [c12]Ingrid von Glehn, James S. Spencer, David Pfau:
A Self-Attention Ansatz for Ab-initio Quantum Chemistry. ICLR 2023 - [i15]Wan Tong Lou, Halvard Sutterud, Gino Cassella, W. Matthew C. Foulkes, Johannes Knolle, David Pfau, James S. Spencer:
Neural Wave Functions for Superfluids. CoRR abs/2305.06989 (2023) - [i14]David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, James S. Spencer:
Natural Quantum Monte Carlo Computation of Excited States. CoRR abs/2308.16848 (2023) - 2022
- [j2]Jonas Degrave
, Federico Felici
Magnetic control of tokamak plasmas through deep reinforcement learning. Nat. 602(7897): 414-419 (2022) - [c11]Richard Evans, Matko Bosnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek J. Sergot:
Making Sense of Raw Input (Extended Abstract). IJCAI 2022: 5727-5731 - [i13]Gino Cassella, Halvard Sutterud, Sam Azadi, N. D. Drummond, David Pfau, James S. Spencer, W. Matthew C. Foulkes:
Discovering Quantum Phase Transitions with Fermionic Neural Networks. CoRR abs/2202.05183 (2022) - [i12]Jan Hermann, James S. Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo
Ab-initio quantum chemistry with neural-network wavefunctions. CoRR abs/2208.12590 (2022) - [i11]Ingrid von Glehn, James S. Spencer, David Pfau:
A Self-Attention Ansatz for Ab-initio Quantum Chemistry. CoRR abs/2211.13672 (2022) - 2020
- [c10]David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière:
Disentangling by Subspace Diffusion. NeurIPS 2020 - [i10]David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière:
Disentangling by Subspace Diffusion. CoRR abs/2006.12982 (2020) - [i9]James S. Spencer, David Pfau, Aleksandar Botev, W. Matthew C. Foulkes:
Better, Faster Fermionic Neural Networks. CoRR abs/2011.07125 (2020) - [i8]David Pfau, Danilo J. Rezende:
Integrable Nonparametric Flows. CoRR abs/2012.02035 (2020)
2010 – 2019
- 2019
- [c9]David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld:
Spectral Inference Networks: Unifying Deep and Spectral Learning. ICLR (Poster) 2019 - [i7]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 abs/1909.02487 (2019) - 2018
- [c8]David Pfau, Christopher P. Burgess:
Minimally Redundant Laplacian Eigenmaps. ICLR (Workshop) 2018 - [i6]David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld:
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning. CoRR abs/1806.02215 (2018) - [i5]Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner:
Towards a Definition of Disentangled Representations. CoRR abs/1812.02230 (2018) - 2017
- [c7]Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. ICLR (Poster) 2017 - 2016
- [c6]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: 109-116 - [c5]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: 3981-3989 - [i4]Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra:
Convolution by Evolution: Differentiable Pattern Producing Networks. CoRR abs/1606.02580 (2016) - [i3]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 abs/1606.04474 (2016) - [i2]David Pfau, Oriol Vinyals:
Connecting Generative Adversarial Networks and Actor-Critic Methods. CoRR abs/1610.01945 (2016) - [i1]Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. CoRR abs/1611.02163 (2016) - 2015
- [j1]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): 394-407 (2015) - 2013
- [c4]David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski:
Robust learning of low-dimensional dynamics from large neural ensembles. NIPS 2013: 2391-2399 - 2012
- [c3]Yan Tat Wong
Decoding arm and hand movements across layers of the macaque frontal cortices. EMBC 2012: 1757-1760 - 2010
- [c2]Nicholas Bartlett, David Pfau, Frank D. Wood:
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. ICML 2010: 63-70 - [c1]David Pfau, Nicholas Bartlett, Frank D. Wood:
Probabilistic Deterministic Infinite Automata. NIPS 2010: 1930-1938
Coauthor Index
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