Geoffrey Hinton - Google Scholar Citations

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James L. McClellandProfessor of Psychology, Stanford UniversityVerified email at stanford.edu
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoVerified email at salk.edu
George E. DahlGoogle BrainVerified email at cs.toronto.edu
Abdelrahman MohamedResearch scientist, Facebook AI ResearchVerified email at fb.com
Vinod NairResearch Scientist, DeepMindVerified email at google.com
Radford NealEmeritus Professor, Dept. of Statistics and Dept. of Computer Science, University of TorontoVerified email at utstat.toronto.edu
Sidney FelsProfessor of Electrical and Computer Engineering, University of British ColumbiaVerified email at ece.ubc.ca
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
David C. PlautProfessor of Psychology, Carnegie Mellon UniversityVerified email at cmu.edu
Demetri TerzopoulosDistinguished Professor & Chancellor's Professor of Computer Science, UCLAVerified email at cs.ucla.edu
Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford UniversityVerified email at stanford.edu
Steven L. SmallUniversity of Texas at DallasVerified email at utdallas.edu
Rama ChellappaProfessor, ECE, Affiliate Professor, CS, UMD, College ParkVerified email at cfar.umd.edu

Geoffrey Hinton

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

Verified email at cs.toronto.edu - Homepage

machine learning psychology artificial intelligence cognitive science computer science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986	65501*	1986
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems, 1097-1105, 2012	59137	2012
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323, 533-536, 1986	44834*	1986
Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Williams CALIFORNIA UNIV SAN DIEGO LA JOLLA INST FOR, 1985	25423	1985
Deep learning Y LeCun, Y Bengio, G Hinton nature 521 (7553), 436-444, 2015	23998	2015
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams nature 323 (6088), 533-536, 1986	20432	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	18553	2014
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	12611	2006
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008	12358	2008
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov science 313 (5786), 504-507, 2006	11734	2006
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Proceedings of the 27th international conference on machine learning (ICML …, 2010	8821	2010
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	7506	2012
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton	6766	2009
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013	5308	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	4788	2012
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002	4340	2002
Adaptive mixtures of local experts RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton Neural computation 3 (1), 79-87, 1991	4168	1991
A learning algorithm for Boltzmann machines DH Ackley, GE Hinton, TJ Sejnowski Cognitive science 9 (1), 147-169, 1985	4094	1985
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 4 (2), 26-31, 2012	3231	2012
Distilling the knowledge in a neural network G Hinton, O Vinyals, J Dean arXiv preprint arXiv:1503.02531, 2015	3097	2015

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