Theophane Weber

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Lars BuesingGoogle DeepMindPatvirtintas el. paštas google.com
Arthur GuezGoogle DeepMindPatvirtintas el. paštas google.com
Peter BattagliaResearch Scientist, DeepMindPatvirtintas el. paštas google.com
Sébastien RacanièreResearch Scientist, DeepMindPatvirtintas el. paštas google.com
David P. ReichertGoogle DeepMindPatvirtintas el. paštas google.com
Timothy P. LillicrapDirector of Research, Google DeepMindPatvirtintas el. paštas google.com
S. M. Ali EslamiGoogle DeepMindPatvirtintas el. paštas google.com
Oriol VinyalsResearch Scientist at Google DeepMindPatvirtintas el. paštas google.com
David SilverDeepMind, UCLPatvirtintas el. paštas google.com
Adrià Puigdomènech BadiaDeepMindPatvirtintas el. paštas google.com
Yujia LiResearch Scientist, Google DeepMindPatvirtintas el. paštas google.com
John SchulmanAnthropicPatvirtintas el. paštas anthropic.com
Justin DauwelsDelft University of TechnologyPatvirtintas el. paštas tudelft.nl
Andrzej CichockiSystems Research Institute, Nicolaus Copernicus University, RIKEN (AIP)Patvirtintas el. paštas riken.jp
David WingateAssociate professor of Computer Science, Brigham Young UniversityPatvirtintas el. paštas cs.byu.edu
David Alan GoldbergAssociate Professor, Operations Research and Information Engineering (ORIE), Cornell UniversityPatvirtintas el. paštas cornell.edu
David GamarnikProfessor of Operations Research, MITPatvirtintas el. paštas mit.edu

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Theophane Weber

Research Scientist at DeepMind

Patvirtintas el. paštas google.com - Pagrindinis puslapis

Artificial Intelligence Machine Learning Reinforcement Learning Probabilistic Modeling.


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	795	2015
Neural scene representation and rendering SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ... Science 360 (6394), 1204-1210, 2018	759	2018
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	739	2017
Attend, infer, repeat: Fast scene understanding with generative models SM Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton Advances in neural information processing systems 29, 2016	622	2016
Gradient estimation using stochastic computation graphs J Schulman, N Heess, T Weber, P Abbeel Advances in neural information processing systems 28, 2015	480	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, 2017	431	2017
Relational recurrent neural networks A Santoro, R Faulkner, D Raposo, J Rae, M Chrzanowski, T Weber, ... Advances in neural information processing systems 31, 2018	278	2018
Automated variational inference in probabilistic programming D Wingate, T Weber arXiv preprint arXiv:1301.1299, 2013	162	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	158	2018
Temporal difference variational auto-encoder K Gregor, G Papamakarios, F Besse, L Buesing, T Weber arXiv preprint arXiv:1806.03107, 2018	156	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	122	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	119	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	102	2018
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	100	2019
On the role of planning in model-based deep reinforcement learning JB Hamrick, AL Friesen, F Behbahani, A Guez, F Viola, S Witherspoon, ... arXiv preprint arXiv:2011.04021, 2020	91	2020
Muesli: Combining improvements in policy optimization M Hessel, I Danihelka, F Viola, A Guez, S Schmitt, L Sifre, T Weber, ... International conference on machine learning, 4214-4226, 2021	88	2021
System linearization T Weber, B Vigoda, P Pratt, J Park, M McCormick US Patent App. 13/678,904, 2013	86	2013
Counterfactual credit assignment in model-free reinforcement learning T Mesnard, T Weber, F Viola, S Thakoor, A Saade, A Harutyunyan, ... arXiv preprint arXiv:2011.09464, 2020	73	2020
Combining q-learning and search with amortized value estimates JB Hamrick, V Bapst, A Sanchez-Gonzalez, T Pfaff, T Weber, L Buesing, ... arXiv preprint arXiv:1912.02807, 2019	63	2019
Learning to induce causal structure NR Ke, S Chiappa, J Wang, A Goyal, J Bornschein, M Rey, T Weber, ... arXiv preprint arXiv:2204.04875, 2022	58	2022

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