Geoffrey Hinton

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Coauteurs

Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoAdresse e-mail validée de salk.edu
George E. DahlGoogle BrainAdresse e-mail validée de cs.toronto.edu
Abdelrahman MohamedResearch scientist, Facebook AI ResearchAdresse e-mail validée de fb.com
Vinod NairResearch Scientist, DeepMindAdresse e-mail validée de google.com
Radford NealEmeritus Professor, Dept. of Statistics and Dept. of Computer Science, University of TorontoAdresse e-mail validée de utstat.toronto.edu
Sidney FelsProfessor of Electrical and Computer Engineering, University of British ColumbiaAdresse e-mail validée de ece.ubc.ca
Chris WilliamsProfessor of Machine Learning, University of EdinburghAdresse e-mail validée de inf.ed.ac.uk
David C. PlautProfessor of Psychology, Carnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Demetri TerzopoulosDistinguished Professor & Chancellor's Professor of Computer Science, UCLAAdresse e-mail validée de cs.ucla.edu
Steven L. SmallUniversity of Texas at DallasAdresse e-mail validée de utdallas.edu

Geoffrey Hinton

Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google

Adresse e-mail validée de cs.toronto.edu - Page d'accueil

machine learning psychology artificial intelligence cognitive science computer science


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Communications of the ACM 60 (6), 84-90, 2017	74353	2017
Deep learning Y LeCun, Y Bengio, G Hinton Nature 521 (7553), 436-444, 2015	32744	2015
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986	27296	1986
Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Wlliams Learning internal representations by error propagation, 1986	26972	1986
Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA 1 (318), 1986	26916	1986
Dropout: a simple way to prevent neural networks from overfitting N Srivastava, G Hinton, A Krizhevsky, I Sutskever, R Salakhutdinov The journal of machine learning research 15 (1), 1929-1958, 2014	24193	2014
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323 (6088), 533-536, 1986	23231	1986
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008	16730	2008
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	14241	2006
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov Science 313 (5786), 504-507, 2006	13543	2006
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton ICML, 2010	11413	2010
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton	9390	2009
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups G Hinton, L Deng, D Yu, GE Dahl, A Mohamed, N Jaitly, A Senior, ... IEEE Signal processing magazine 29 (6), 82-97, 2012	8869	2012
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013	6581	2013
Improving neural networks by preventing co-adaptation of feature detectors GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov arXiv preprint arXiv:1207.0580, 2012	5782	2012
Distilling the knowledge in a neural network G Hinton, O Vinyals, J Dean arXiv preprint arXiv:1503.02531, 2015	4835	2015
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002	4756	2002
Adaptive mixtures of local experts RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton Neural computation 3 (1), 79-87, 1991	4383	1991
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude T Tieleman, G Hinton Coursera: Neural networks for machine learning, 2012	4355	2012
A learning algorithm for Boltzmann machines DH Ackley, GE Hinton, TJ Sejnowski Cognitive science 9 (1), 147-169, 1985	4292	1985

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