Geoffrey Hinton - Citazioni di Google Scholar

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James L. McClellandProfessor of Psychology, Stanford UniversityEmail verificata su stanford.edu
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoEmail verificata su salk.edu
George E. DahlGoogle BrainEmail verificata su cs.toronto.edu
Abdelrahman MohamedResearch scientist, Facebook AI ResearchEmail verificata su fb.com
Vinod NairResearch Scientist, DeepMindEmail verificata su google.com
Radford NealEmeritus Professor, Dept. of Statistics and Dept. of Computer Science, University of TorontoEmail verificata su utstat.toronto.edu
Sidney FelsProfessor of Electrical and Computer Engineering, University of British ColumbiaEmail verificata su ece.ubc.ca
Chris WilliamsProfessor of Machine Learning, University of EdinburghEmail verificata su inf.ed.ac.uk
David C. PlautProfessor of Psychology, Carnegie Mellon UniversityEmail verificata su cmu.edu
Demetri TerzopoulosDistinguished Professor & Chancellor's Professor of Computer Science, UCLAEmail verificata su cs.ucla.edu
Robert TibshiraniProfessor of Biomedical Data Sciences, and of Statistics, Stanford UniversityEmail verificata su stanford.edu
Steven L. SmallUniversity of Texas at DallasEmail verificata su utdallas.edu
Rama ChellappaProfessor, ECE, Affiliate Professor, CS, UMD, College ParkEmail verificata su cfar.umd.edu

Geoffrey Hinton

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

Email verificata su cs.toronto.edu - Home page

machine learning neural networks artificial intelligence cognitive science computer science


Titolo	Citata da	Anno
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986	63057*	1986
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems, 1097-1105, 2012	47534	2012
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323, 533-536, 1986	42471*	1986
Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Williams CALIFORNIA UNIV SAN DIEGO LA JOLLA INST FOR, 1985	24876	1985
Deep learning Y LeCun, Y Bengio, G Hinton nature 521 (7553), 436, 2015	18912	2015
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Cognitive modeling 5 (3), 1, 1988	18593	1988
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	14613	2014
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006	11137	2006
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov science 313 (5786), 504-507, 2006	10224	2006
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008	9782	2008
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Proceedings of the 27th international conference on machine learning (ICML …, 2010	7023	2010
Deep neural networks for acoustic modeling in speech recognition G Hinton, L Deng, D Yu, G Dahl, A Mohamed, N Jaitly, A Senior, ... IEEE Signal processing magazine 29, 2012	6449	2012
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton Technical report, University of Toronto 1 (4), 7, 2009	5072	2009
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013	4346	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	4248	2012
Adaptive mixtures of local experts. RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton Neural computation 3 (1), 79-87, 1991	4038	1991
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002	4016	2002
A learning algorithm for Boltzmann machines DH Ackley, GE Hinton, TJ Sejnowski Cognitive science 9 (1), 147-169, 1985	3896	1985
Phoneme recognition using time-delay neural networks A Waibel, T Hanazawa, G Hinton, K Shikano, KJ Lang IEEE transactions on acoustics, speech, and signal processing 37 (3), 328-339, 1989	2844	1989
A view of the EM algorithm that justifies incremental, sparse, and other variants RM Neal, GE Hinton Learning in graphical models, 355-368, 1998	2742	1998

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