Theophane Weber
Theophane Weber
Research Scientist at DeepMind
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Citado por
Citado por
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
Neural scene representation and rendering
SMA Eslami, DJ Rezende, F Besse, F Viola, AS Morcos, M Garnelo, ...
Science 360 (6394), 1204-1210, 2018
Gradient estimation using stochastic computation graphs
J Schulman, N Heess, T Weber, P Abbeel
Advances in Neural Information Processing Systems 28, 3528-3536, 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
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
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
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
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
Automated variational inference in probabilistic programming
D Wingate, T Weber
arXiv preprint arXiv:1301.1299, 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
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
System linearization
T Weber, B Vigoda, P Pratt, J Park, M McCormick
US Patent App. 13/678,904, 2013
Temporal difference variational auto-encoder
K Gregor, G Papamakarios, F Besse, L Buesing, T Weber
arXiv preprint arXiv:1806.03107, 2018
Learning to search with MCTSnets
A Guez, T Weber, I Antonoglou, K Simonyan, O Vinyals, D Wierstra, ...
arXiv preprint arXiv:1802.04697, 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
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
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
On similarity measures for spike trains
J Dauwels, F Vialatte, T Weber, A Cichocki
International Conference on Neural Information Processing, 177-185, 2008
Credit assignment techniques in stochastic computation graphs
T Weber, N Heess, L Buesing, D Silver
arXiv preprint arXiv:1901.01761, 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
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Artículos 1–20