| 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 | 1897 | 2016 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein arXiv preprint arXiv:1611.02163, 2016 | 1071 | 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 | 897 | 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 | 389 | 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 | 316 | 2019 |
| 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 | 283 | 2022 |
| Connecting generative adversarial networks and actor-critic methods D Pfau, O Vinyals arXiv preprint arXiv:1610.01945, 2016 | 192 | 2016 |
| 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 | 155 | 2021 |
| 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 | 120 | 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 | 72 | 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 | 70 | 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 | 48 | 2014 |
| Spectral inference networks: Unifying deep and spectral learning D Pfau, S Petersen, A Agarwal, DGT Barrett, KL Stachenfeld arXiv preprint arXiv:1806.02215, 2018 | 37* | 2018 |
| Probabilistic deterministic infinite automata D Pfau, N Bartlett, F Wood Advances in neural information processing systems 23, 2010 | 29 | 2010 |
| Better, faster fermionic neural networks JS Spencer, D Pfau, A Botev, WMC Foulkes arXiv preprint arXiv:2011.07125, 2020 | 26 | 2020 |
| Disentangling by subspace diffusion D Pfau, I Higgins, A Botev, S Racanière Advances in Neural Information Processing Systems 33, 17403-17415, 2020 | 21 | 2020 |
| Making sense of raw input R Evans, M Bošnjak, L Buesing, K Ellis, D Pfau, P Kohli, M Sergot Artificial Intelligence 299, 103521, 2021 | 18 | 2021 |
| 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 | 17 | 2023 |
| 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 | 16 | 2010 |
| A generalized bias-variance decomposition for bregman divergences D Pfau Unpublished Manuscript, 2013 | 14 | 2013 |
Liam PaninskiColumbia University在 stat.columbia.edu 的电子邮件经过验证
