| Imagination-augmented agents for deep reinforcement learning T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv preprint arXiv:1707.06203, 2017 | 371* | 2017 |
| Attend, infer, repeat: Fast scene understanding with generative models SM Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, K Kavukcuoglu, ... arXiv preprint arXiv:1603.08575, 2016 | 287 | 2016 |
| Neural scene representation and rendering SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ... Science 360 (6394), 1204-1210, 2018 | 285 | 2018 |
| Gradient estimation using stochastic computation graphs J Schulman, N Heess, T Weber, P Abbeel arXiv preprint arXiv:1506.05254, 2015 | 258 | 2015 |
| Deep reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ... arXiv preprint arXiv:1512.07679, 2015 | 195 | 2015 |
| Visual interaction networks: Learning a physics simulator from video N Watters, D Zoran, T Weber, P Battaglia, R Pascanu, A Tacchetti Advances in neural information processing systems 30, 4539-4547, 2017 | 137 | 2017 |
| Relational recurrent neural networks A Santoro, R Faulkner, D Raposo, J Rae, M Chrzanowski, T Weber, ... arXiv preprint arXiv:1806.01822, 2018 | 112 | 2018 |
| Automated variational inference in probabilistic programming D Wingate, T Weber arXiv preprint arXiv:1301.1299, 2013 | 99 | 2013 |
| Visual interaction networks N Watters, A Tacchetti, T Weber, R Pascanu, P Battaglia, D Zoran arXiv preprint arXiv:1706.01433, 2017 | 88 | 2017 |
| 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 | 80 | 2017 |
| Temporal difference variational auto-encoder K Gregor, G Papamakarios, F Besse, L Buesing, T Weber arXiv preprint arXiv:1806.03107, 2018 | 59 | 2018 |
| 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 | 59 | 2018 |
| System linearization T Weber, B Vigoda, P Pratt, J Park, M Mccormick US Patent App. 13/678,904, 2013 | 57 | 2013 |
| 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 | 49 | 2018 |
| Learning to search with MCTSnets A Guez, T Weber, I Antonoglou, K Simonyan, O Vinyals, D Wierstra, ... International Conference on Machine Learning, 1822-1831, 2018 | 47 | 2018 |
| Quantifying statistical interdependence by message passing on graphs—part II: multidimensional point processes J Dauwels, F Vialatte, T Weber, T Musha, A Cichocki Neural computation 21 (8), 2203-2268, 2009 | 35 | 2009 |
| 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 | 33 | 2019 |
| On similarity measures for spike trains J Dauwels, F Vialatte, T Weber, A Cichocki International Conference on Neural Information Processing, 177-185, 2008 | 32 | 2008 |
| Credit assignment techniques in stochastic computation graphs T Weber, N Heess, L Buesing, D Silver The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 27 | 2019 |
| To wave or not to wave? Order release policies for warehouses with an automated sorter J Gallien, T Weber Manufacturing & Service Operations Management 12 (4), 642-662, 2010 | 20 | 2010 |
Lars BuesingGoogle DeepMindVerified email at google.com
