Geoffrey Hinton 个人学术档案

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	总计	2019 年至今
引用	813608	522784
h 指数	182	131
i10 指数	466	343

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Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San Diego在 salk.edu 的电子邮件经过验证
George E. DahlGoogle Inc.在 google.com 的电子邮件经过验证
Vinod NairResearch Scientist, DeepMind在 google.com 的电子邮件经过验证
Abdelrahman MohamedResearch scientist, Facebook AI Research在 fb.com 的电子邮件经过验证
Radford NealU Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!在 utstat.toronto.edu 的电子邮件经过验证
Sidney FelsProfessor of Electrical and Computer Engineering, University of British Columbia在 ece.ubc.ca 的电子邮件经过验证
David C. PlautProfessor of Psychology, Carnegie Mellon University在 cmu.edu 的电子邮件经过验证
Chris WilliamsProfessor of Machine Learning, University of Edinburgh在 inf.ed.ac.uk 的电子邮件经过验证
Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford University在 stanford.edu 的电子邮件经过验证
Demetri TerzopoulosDistinguished Professor & Chancellor's Professor of Computer Science, UCLA在 cs.ucla.edu 的电子邮件经过验证
Steven L. SmallUniversity of Texas at Dallas在 utdallas.edu 的电子邮件经过验证

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Geoffrey Hinton

Emeritus Prof. Computer Science, University of Toronto

在 cs.toronto.edu 的电子邮件经过验证 - 首页

machine learning psychology artificial intelligence cognitive science computer science


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 2012	156962*	2012
Deep learning Y LeCun, Y Bengio, G Hinton Nature 521 (7553), 436-44, 2015	78481	2015
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986	58377*	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	50858	2014
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008	46903	2008
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323 (6088), 533-536, 1986	38223	1986
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton	29998	2009
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Proceedings of the 27th international conference on machine learning (ICML …, 2010	24993	2010
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov Science 313 (5786), 504-507, 2006	22816	2006
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	21392	2006
Distilling the knowledge in a neural network G Hinton, O Vinyals, J Dean arXiv preprint arXiv:1503.02531, 2015	19647	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	17230	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	13344	2012
Layer normalization JL Ba, JR Kiros, GE Hinton arXiv preprint arXiv:1607.06450, 2016	11782	2016
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013	11444	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	11244	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	7804	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	7509*	1986
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002	6459	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	6219	2013

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