Geoffrey Hinton

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Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoVerified email at salk.edu
George E. DahlGoogle Inc.Verified email at google.com
Abdelrahman MohamedResearch scientist, Facebook AI ResearchVerified email at fb.com
Vinod NairResearch Scientist, DeepMindVerified email at google.com
Radford NealU Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!Verified 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

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


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» Simulating Brain « GE Hinton, DC Plaut, T Shallice Scientific American, 1993	1	1993
//Proc. Advances in Neural Inform. A Krizhevsky, I Sutskever, G Hinton Proces. Systems. 2012. V. 25. 25, 1090, 2012	16	2012
& Salakhutdinov, R.(2006). Reducing the dimensionality of data with neural networks G Hinton Science 313, 0	5
1., and Revow, MD (1992a). Adaptive elastic models for hand-printed character recognition GE Hinton, CK Williams Advances in Neural Information Processing Systems 4, 0	5
15 Learning to Use Spike Timing in a Restricted Boltzmann Machine GE Hinton, AD Brown Probabilistic Models of the Brain, 285, 2002	3	2002
3AR, UK Z Ghahramani, GE Hinton Graphical Models: Foundations of Neural Computation, 315, 2001		2001
3D object recognition with deep belief nets V Nair, GE Hinton Advances in neural information processing systems 22, 1339-1347, 2009	396	2009
8: McClel1and, JL (I986). A general framework for parallel distributed processing DE Rumelhart, GE Hinton Parallel distributed processing: Explorations in the microstructure of …, 0	5
8L Williams, R.(1986) DE Rumelhart, GE Hinton Learning internal representations by error, 0	2
A better way to learn features: technical perspective GE Hinton Communications of the ACM 54 (10), 94-94, 2011	19	2011
A better way to pretrain deep boltzmann machines GE Hinton, RR Salakhutdinov Advances in Neural Information Processing Systems 25, 2447-2455, 2012	131	2012
A comparison of classification methods for longitudinal fmri studies T Schmah, G Yourganov, RS Zemel, GE Hinton, SL Small, S Strother Neuroimage, S57, 2009	2	2009
A comparison of statistical learning methods on the GUSTO database M Ennis, G Hinton, D Naylor, M Revow, R Tibshirani Statistics in medicine 17 (21), 2501-2508, 1998	95	1998
A connectionist model of visual memory J Feldman, G Hinton, J Anderson Parallel Models of Associative Memory: Updated Edition, 65, 1981	151	1981
A desktop input device and interface for interactive 3d character animation S Oore, D Terzopoulos, G Hinton Graphics Interface 2, 133-140, 2002	70	2002
A distributed connectionist production system DS Touretzky, GE Hinton Cognitive science 12 (3), 423-466, 1988	461	1988
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	15267	2006
A general framework for parallel distributed processing DE Rumelhart, GE Hinton, JL McClelland Parallel distributed processing: Explorations in the microstructure of …, 1986	1336	1986
A hierarchical community of experts GE Hinton, B Sallans, Z Ghahramani Learning in graphical models, 479-494, 1998	57*	1998
A learning algorithm for Boltzmann machines DH Ackley, GE Hinton, TJ Sejnowski Cognitive science 9 (1), 147-169, 1985	4497	1985

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