Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein arXiv preprint arXiv:1611.02163, 2016 | 1300* | 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, 3981-3989, 2016 | 640 | 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 | 375 | 2016 |

Connecting generative adversarial networks and actor-critic methods D Pfau, O Vinyals arXiv preprint arXiv:1610.01945, 2016 | 82 | 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 | 66 | 2016 |

Robust learning of low-dimensional dynamics from large neural ensembles D Pfau, EA Pnevmatikakis, L Paninski Advances in neural information processing systems, 2391-2399, 2013 | 47 | 2013 |

Towards a definition of disentangled representations I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ... arXiv preprint arXiv:1812.02230, 2018 | 41 | 2018 |

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 | 36 | 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 | 29 | 2014 |

Probabilistic deterministic infinite automata D Pfau, N Bartlett, F Wood Advances in neural information processing systems, 1930-1938, 2010 | 18 | 2010 |

Forgetting counts: Constant memory inference for a dependent hierarchical Pitman-Yor process N Bartlett, D Pfau, F Wood Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 13 | 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 | 8 | 2018 |

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 | 7* | 2019 |

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 | 5 | 2012 |

C Lacefield C, W Yang, M Ahrens, R Bruno, TM Jessell, DS Peterka, R Yuste, L Paninski,“Simultaneous denoising, deconvolution, and demixing of calcium imaging data EA Pnevmatikakis, D Soudry, Y Gao, TA Machado, J Merel, D Pfau, ... Neuron 89, 285-299, 2016 | 3 | 2016 |

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 | 2 | 2012 |

A Bayesian method to predict the optimal diffusion coefficient in random fixational eye movements D Pfau, X Pitkow, L Paninski Conference abstract: Computational and systems neuroscience, 2009 | 2 | 2009 |

A structured matrix factorization framework for large scale calcium imaging data analysis K Poskanzer, EA Pnevmatikakis, Y Gao, D Soudry, D Pfau, C Lacefield, ... | 1 | 2014 |

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks J Spencer, D Pfau, A Matthews, WM Foulkes Bulletin of the American Physical Society, 2020 | | 2020 |

Training machine learning models MMR Denil, T Schaul, M Andrychowicz, JFG De Freitas, SG Colmenarejo, ... US Patent App. 16/302,592, 2019 | | 2019 |