Geoffrey Hinton
Geoffrey Hinton
Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Imagenet classification with deep convolutional neural networks
A Krizhevsky, I Sutskever, GE Hinton
Advances in neural information processing systems 25, 1097-1105, 2012
938762012
Deep learning
Y LeCun, Y Bengio, G Hinton
Nature 521 (7553), 436-444, 2015
424102015
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
307652014
Learning internal representations by error-propagation
DE Rumelhart, GE Hinton, RJ Williams
Parallel Distributed Processing: Explorations in the Microstructure of …, 1986
291611986
Learning internal representations by error propagation
DE Rumelhart, GE Hinton, RJ Wlliams
Learning internal representations by error propagation, 1986
275721986
Learning internal representations by error propagation
DE Rumelhart, GE Hinton, RJ Williams
MIT Press, Cambridge, MA 1 (318), 1986
275211986
Learning representations by back-propagating errors
DE Rumelhart, GE Hinton, RJ Williams
Nature 323 (6088), 533-536, 1986
258541986
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
24738*1986
Visualizing data using t-SNE
L van der Maaten, G Hinton
Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008
223092008
A fast learning algorithm for deep belief nets
GE Hinton, S Osindero, YW Teh
Neural computation 18 (7), 1527-1554, 2006
162852006
Reducing the dimensionality of data with neural networks
GE Hinton, RR Salakhutdinov
Science 313 (5786), 504-507, 2006
160142006
Rectified linear units improve restricted boltzmann machines
V Nair, GE Hinton
Icml, 2010
145122010
Learning multiple layers of features from tiny images
A Krizhevsky, G Hinton
128002009
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
102792012
Speech recognition with deep recurrent neural networks
A Graves, A Mohamed, G Hinton
2013 IEEE international conference on acoustics, speech and signal …, 2013
79752013
Distilling the knowledge in a neural network
G Hinton, O Vinyals, J Dean
arXiv preprint arXiv:1503.02531, 2015
72992015
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
69162012
Training products of experts by minimizing contrastive divergence
GE Hinton
Neural computation 14 (8), 1771-1800, 2002
52612002
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
52402012
Adaptive mixtures of local experts
RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton
Neural computation 3 (1), 79-87, 1991
47401991
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