Thore Graepel

Citado por

	Total	Desde 2019
Citas	67241	49578
Índice h	75	52
Índice i10	173	125

10000

5000

2500

7500

2003200420052006200720082009201020112012201320142015201620172018201920202021202220232024168 174 252 295 349 345 420 387 504 542 765 1004 1102 2015 2906 5265 7042 8034 9038 9202 9565 6660

Coautores

Ralf HerbrichHasso Plattner InstituteDirección de correo verificada de hpi.de
Marc LanctotResearch Scientist, Google DeepMindDirección de correo verificada de google.com
David SilverDeepMind, UCLDirección de correo verificada de google.com
Laurent SifreGoogle DeepMindDirección de correo verificada de polytechnique.edu
Arthur GuezGoogle DeepMindDirección de correo verificada de google.com
Joel Z LeiboResearch scientistDirección de correo verificada de google.com
Timothy P. LillicrapDirector of Research, Google DeepMindDirección de correo verificada de google.com
Yoram BachrachDeepMindDirección de correo verificada de google.com
Klaus ObermayerProfessor, Fakultät IV - Electrical Engineering and Computer Science, Technische Universität BerlinDirección de correo verificada de tu-berlin.de
Karl TuylsResearch Scientist, Entrepreneur, ex-Google DeepMind, Prof at University of LiverpoolDirección de correo verificada de hcompany.ai
Thomas HubertGoogle DeepmindDirección de correo verificada de google.com
Michal KosinskiStanford UniversityDirección de correo verificada de stanford.edu
Karen SimonyanChief Scientist, Microsoft AIDirección de correo verificada de microsoft.com
Chris J. MaddisonUniversity of TorontoDirección de correo verificada de cs.toronto.edu
Matthew LaiDeepMindDirección de correo verificada de google.com
David StillwellCambridge UniversityDirección de correo verificada de cam.ac.uk
koray kavukcuogluDeepMindDirección de correo verificada de kavukcuoglu.org
Aja HuangDeepMindDirección de correo verificada de google.com
julien perolatDeepMindDirección de correo verificada de google.com
Vinicius ZambaldiGoogle DeepmindDirección de correo verificada de google.com

Seguir

Thore Graepel

Global Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCL

Dirección de correo verificada de ucl.ac.uk - Página principal

Machine Learning Reinforcement Learning Deep Learning Multi-Agent Learning Computational Biology


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... nature 529 (7587), 484-489, 2016	19966	2016
Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, ... nature 550 (7676), 354-359, 2017	11379	2017
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... Science 362 (6419), 1140-1144, 2018	4751	2018
Private traits and attributes are predictable from digital records of human behavior M Kosinski, D Stillwell, T Graepel Proceedings of the national academy of sciences 110 (15), 5802-5805, 2013	3952	2013
Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, ... Nature 588 (7839), 604-609, 2020	2486	2020
Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... arXiv preprint arXiv:1712.01815, 2017	2325	2017
Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017	1856	2017
Large margin rank boundaries for ordinal regression R Herbrich, T Graepel, K Obermayer	1472	2000
TrueSkill™: a Bayesian skill rating system R Herbrich, T Minka, T Graepel Advances in neural information processing systems 19, 2006	1090	2006
Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... Science 364 (6443), 859-865, 2019	983	2019
Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017	906	2017
A unified game-theoretic approach to multiagent reinforcement learning M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ... Advances in neural information processing systems 30, 2017	754	2017
Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine T Graepel, JQ Candela, T Borchert, R Herbrich Omnipress 27, 13-20, 2010	742	2010
Personality and patterns of Facebook usage Y Bachrach, M Kosinski, T Graepel, P Kohli, D Stillwell Proceedings of the 4th annual ACM web science conference, 24-32, 2012	669	2012
ML confidential: Machine learning on encrypted data T Graepel, K Lauter, M Naehrig International conference on information security and cryptology, 1-21, 2012	645	2012
Support vector learning for ordinal regression R Herbrich, T Graepel, K Obermayer IET Digital Library, 1999	583	1999
Manifestations of user personality in website choice and behaviour on online social networks M Kosinski, Y Bachrach, P Kohli, D Stillwell, T Graepel Machine learning 95, 357-380, 2014	447	2014
Matchbox: large scale online bayesian recommendations DH Stern, R Herbrich, T Graepel Proceedings of the 18th international conference on World wide web, 111-120, 2009	367	2009
Bayes point machines R Herbrich, T Graepel, C Campbell Journal of Machine Learning Research 1 (Aug), 245-279, 2001	339	2001
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	324	2018

El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.

Artículos 1–20

Citas por año

Citas duplicadas

Citas combinadas

Añadir coautoresCoautores

Seguir

Citado por

Coautores