| Imagination-augmented agents for deep reinforcement learning S Racanière, T Weber, D Reichert, L Buesing, A Guez, ... Advances in neural information processing systems 30, 2017 | 598* | 2017 |
| Towards a definition of disentangled representations I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ... arXiv preprint arXiv:1812.02230, 2018 | 385 | 2018 |
| The mechanics of n-player differentiable games D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel International Conference on Machine Learning, 354-363, 2018 | 272 | 2018 |
| Recurrent environment simulators S Chiappa, S Racaniere, D Wierstra, S Mohamed arXiv preprint arXiv:1704.02254, 2017 | 198 | 2017 |
| Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins arXiv preprint arXiv:1909.13789, 2019 | 168 | 2019 |
| Equivariant flow-based sampling for lattice gauge theory G Kanwar, MS Albergo, D Boyda, K Cranmer, DC Hackett, S Racaniere, ... Physical Review Letters 125 (12), 121601, 2020 | 163 | 2020 |
| Learning and querying fast generative models for reinforcement learning L Buesing, T Weber, S Racaniere, SM Eslami, D Rezende, DP Reichert, ... arXiv preprint arXiv:1802.03006, 2018 | 123 | 2018 |
| Sampling using gauge equivariant flows D Boyda, G Kanwar, S Racanière, DJ Rezende, MS Albergo, K Cranmer, ... Physical Review D 103 (7), 074504, 2021 | 113 | 2021 |
| Woulda, coulda, shoulda: Counterfactually-guided policy search L Buesing, T Weber, Y Zwols, S Racaniere, A Guez, JB Lespiau, N Heess arXiv preprint arXiv:1811.06272, 2018 | 112 | 2018 |
| Learning model-based planning from scratch R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ... arXiv preprint arXiv:1707.06170, 2017 | 111 | 2017 |
| Normalizing flows on tori and spheres DJ Rezende, G Papamakarios, S Racaniere, M Albergo, G Kanwar, ... International Conference on Machine Learning, 8083-8092, 2020 | 101 | 2020 |
| An investigation of model-free planning A Guez, M Mirza, K Gregor, R Kabra, S Racanière, T Weber, D Raposo, ... International Conference on Machine Learning, 2464-2473, 2019 | 76 | 2019 |
| Differentiable game mechanics A Letcher, D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, ... The Journal of Machine Learning Research 20 (1), 3032-3071, 2019 | 74 | 2019 |
| Automated curricula through setter-solver interactions S Racaniere, AK Lampinen, A Santoro, DP Reichert, V Firoiu, TP Lillicrap arXiv preprint arXiv:1909.12892, 2019 | 70 | 2019 |
| Targeted free energy estimation via learned mappings P Wirnsberger, AJ Ballard, G Papamakarios, S Abercrombie, S Racanière, ... The Journal of Chemical Physics 153 (14), 144112, 2020 | 58 | 2020 |
| Rapid computation of value and risk for derivatives portfolios S Weston, J Spooner, S Racanière, O Mencer Concurrency and Computation: Practice and Experience 24 (8), 880-894, 2012 | 53 | 2012 |
| Equivariant hamiltonian flows DJ Rezende, S Racanière, I Higgins, P Toth arXiv preprint arXiv:1909.13739, 2019 | 49 | 2019 |
| Accelerating large-scale HPC Applications using FPGAs R Dimond, S Racaniere, O Pell 2011 IEEE 20th Symposium on Computer Arithmetic, 191-192, 2011 | 46 | 2011 |
| Flow-based sampling for fermionic lattice field theories MS Albergo, G Kanwar, S Racaniere, DJ Rezende, JM Urban, D Boyda, ... Physical Review D 104 (11), 114507, 2021 | 34 | 2021 |
| Introduction to normalizing flows for lattice field theory MS Albergo, D Boyda, DC Hackett, G Kanwar, K Cranmer, S Racaniere, ... arXiv preprint arXiv:2101.08176, 2021 | 24 | 2021 |
Danilo J. RezendeSenior Staff Research Scientist at DeepMind在 google.com 的电子邮件经过验证
