Geoffrey Hinton

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

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Geoffrey Hinton

Emeritus Prof. Computer Science, University of Toronto

Patvirtintas el. paštas cs.toronto.edu - Pagrindinis puslapis

machine learning psychology artificial intelligence cognitive science computer science


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 2012	161985*	2012
Deep learning Y LeCun, Y Bengio, G Hinton Nature 521 (7553), 436-44, 2015	83800	2015
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986	54841*	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	52402	2014
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008	47043	2008
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323 (6088), 533-536, 1986	39294	1986
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton	31505	2009
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Proceedings of the 27th international conference on machine learning (ICML …, 2010	25632	2010
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov Science 313 (5786), 504-507, 2006	23408	2006
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	21525	2006
Distilling the Knowledge in a Neural Network G Hinton arXiv preprint arXiv:1503.02531, 2015	20911	2015
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	18943	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	13807	2012
Layer normalization JL Ba arXiv preprint arXiv:1607.06450, 2016	12481	2016
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013	11645	2013
Improving neural networks by preventing co-adaptation of feature detectors GE Hinton arXiv preprint arXiv:1207.0580, 2012	11526	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	8003	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	6835*	1986
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002	6543	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	6383	2013

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