Geoffrey Hinton

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Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoPreverjeni e-poštni naslov na salk.edu
Vinod NairResearch Scientist, DeepMindPreverjeni e-poštni naslov na google.com
George E. DahlGoogle Inc.Preverjeni e-poštni naslov na google.com
Abdelrahman MohamedResearch scientist, Facebook AI ResearchPreverjeni e-poštni naslov na fb.com
Radford NealU Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!Preverjeni e-poštni naslov na utstat.toronto.edu
Sidney FelsProfessor of Electrical and Computer Engineering, University of British ColumbiaPreverjeni e-poštni naslov na ece.ubc.ca
David C. PlautProfessor of Psychology, Carnegie Mellon UniversityPreverjeni e-poštni naslov na cmu.edu
Chris WilliamsProfessor of Machine Learning, University of EdinburghPreverjeni e-poštni naslov na inf.ed.ac.uk
Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Demetri TerzopoulosDistinguished Professor & Chancellor's Professor of Computer Science, UCLAPreverjeni e-poštni naslov na cs.ucla.edu
Steven L. SmallUniversity of Texas at DallasPreverjeni e-poštni naslov na utdallas.edu

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Geoffrey Hinton

Emeritus Prof. Computer Science, University of Toronto

Preverjeni e-poštni naslov na cs.toronto.edu - Domača stran

machine learning psychology artificial intelligence cognitive science computer science


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 2012	167428*	2012
Deep learning Y LeCun, Y Bengio, G Hinton Nature 521 (7553), 436-44, 2015	88265	2015
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986	55363*	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	54293	2014
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008	49993	2008
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323 (6088), 533-536, 1986	40794	1986
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton	33524	2009
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Proceedings of the 27th international conference on machine learning (ICML …, 2010	26440	2010
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov Science 313 (5786), 504-507, 2006	24075	2006
Distilling the Knowledge in a Neural Network G Hinton arXiv preprint arXiv:1503.02531, 2015	22566	2015
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	21996	2006
A simple framework for contrastive learning of visual representations T Chen, S Kornblith, M Norouzi, G Hinton International conference on machine learning, 1597-1607, 2020	20959	2020
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	14145	2012
Layer normalization JL Ba arXiv preprint arXiv:1607.06450, 2016	13465	2016
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013	11960	2013
Improving neural networks by preventing co-adaptation of feature detectors GE Hinton arXiv preprint arXiv:1207.0580, 2012	11803	2012
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	8231	2012
Schemata and sequential thought processes in PDP models. D Rumelhart, P Smolenksy, J McClelland, G Hinton Parallel distributed processing: Explorations in the microstructure of …, 1986	6908*	1986
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002	6669	2002
On the importance of initialization and momentum in deep learning I Sutskever, J Martens, G Dahl, G Hinton International conference on machine learning, 1139-1147, 2013	6616	2013

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