| Attend, infer, repeat: Fast scene understanding with generative models SMA Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton Advances in Neural Information Processing Systems, 3225-3233, 2016 | 250 | 2016 |
| Gradient estimation using stochastic computation graphs J Schulman, N Heess, T Weber, P Abbeel Advances in Neural Information Processing Systems, 3528-3536, 2015 | 235 | 2015 |
| Neural scene representation and rendering SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ... Science 360 (6394), 1204-1210, 2018 | 230 | 2018 |
| 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 | 202 | 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, 4539-4547, 2017 | 193 | 2017 |
| Imagination-augmented agents for deep reinforcement learning S Racanière, T Weber, D Reichert, L Buesing, A Guez, DJ Rezende, ... Advances in neural information processing systems, 5690-5701, 2017 | 167 | 2017 |
| 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 | 162 | 2017 |
| Relational recurrent neural networks A Santoro, R Faulkner, D Raposo, J Rae, M Chrzanowski, T Weber, ... Advances in neural information processing systems, 7299-7310, 2018 | 90 | 2018 |
| Automated variational inference in probabilistic programming D Wingate, T Weber arXiv preprint arXiv:1301.1299, 2013 | 82 | 2013 |
| 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 | 72 | 2017 |
| System linearization T Weber, B Vigoda, P Pratt, J Park, M McCormick US Patent App. 13/678,904, 2013 | 53 | 2013 |
| 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 | 49 | 2018 |
| Temporal difference variational auto-encoder K Gregor, G Papamakarios, F Besse, L Buesing, T Weber arXiv preprint arXiv:1806.03107, 2018 | 41 | 2018 |
| 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 | 37 | 2018 |
| Learning to search with MCTSnets A Guez, T Weber, I Antonoglou, K Simonyan, O Vinyals, D Wierstra, ... arXiv preprint arXiv:1802.04697, 2018 | 37 | 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 | 33 | 2009 |
| On similarity measures for spike trains J Dauwels, F Vialatte, T Weber, A Cichocki International Conference on Neural Information Processing, 177-185, 2008 | 31 | 2008 |
| An investigation of model-free planning A Guez, M Mirza, K Gregor, R Kabra, S Racanière, T Weber, D Raposo, ... arXiv preprint arXiv:1901.03559, 2019 | 20 | 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 | 19 | 2010 |
| Credit assignment techniques in stochastic computation graphs T Weber, N Heess, L Buesing, D Silver arXiv preprint arXiv:1901.01761, 2019 | 18 | 2019 |
Lars BuesingGoogle DeepMindVerified email at google.com
