| Learning to learn by gradient descent by gradient descent M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ... arXiv preprint arXiv:1606.04474, 2016 | 1188 | 2016 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein arXiv preprint arXiv:1611.02163, 2016 | 704 | 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 | 614 | 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 | 141 | 2018 |
| Connecting generative adversarial networks and actor-critic methods D Pfau, O Vinyals arXiv preprint arXiv:1610.01945, 2016 | 129 | 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 | 92 | 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 | 75 | 2019 |
| Robust learning of low-dimensional dynamics from large neural ensembles. D Pfau, EA Pnevmatikakis, L Paninski NIPS 2013, 2391-2399, 2013 | 57 | 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 | 56 | 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 | 37 | 2014 |
| Probabilistic Deterministic Infinite Automata. D Pfau, N Bartlett, FD Wood NIPS, 1930-1938, 2010 | 25 | 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 | 19* | 2018 |
| Forgetting counts: Constant memory inference for a dependent hierarchical Pitman-Yor process N Bartlett, D Pfau, FD Wood ICML, 2010 | 15 | 2010 |
| Disentangling by Subspace Diffusion D Pfau, I Higgins, A Botev, S Racaničre arXiv preprint arXiv:2006.12982, 2020 | 8 | 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 | 7 | 2012 |
| Better, faster fermionic neural networks JS Spencer, D Pfau, A Botev, WMC Foulkes arXiv preprint arXiv:2011.07125, 2020 | 4 | 2020 |
| Training machine learning models MMR Denil, T Schaul, M Andrychowicz, JFG De Freitas, SG Colmenarejo, ... US Patent App. 16/302,592, 2019 | 3 | 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 | 3 | 2012 |
| A structured matrix factorization framework for large scale calcium imaging data analysis K Poskanzer, EA Pnevmatikakis, Y Gao, D Soudry, D Pfau, C Lacefield, ... | 2 | 2014 |
| 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 |
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
