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
907122012
Deep learning
Y LeCun, Y Bengio, G Hinton
Nature 521 (7553), 436-444, 2015
406332015
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
296482014
Learning internal representations by error-propagation
DE Rumelhart, GE Hinton, RJ Williams
Parallel Distributed Processing: Explorations in the Microstructure of …, 1986
289141986
Learning internal representations by error propagation
DE Rumelhart, GE Hinton, RJ Wlliams
Learning internal representations by error propagation, 1986
273371986
Learning internal representations by error propagation
DE Rumelhart, GE Hinton, RJ Williams
MIT Press, Cambridge, MA 1 (318), 1986
272861986
Learning representations by back-propagating errors
DE Rumelhart, GE Hinton, RJ Williams
Nature 323 (6088), 533-536, 1986
253311986
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
24623*1986
Visualizing data using t-SNE
L van der Maaten, G Hinton
Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008
212662008
A fast learning algorithm for deep belief nets
GE Hinton, S Osindero, YW Teh
Neural computation 18 (7), 1527-1554, 2006
160012006
Reducing the dimensionality of data with neural networks
GE Hinton, RR Salakhutdinov
Science 313 (5786), 504-507, 2006
156152006
Rectified linear units improve restricted boltzmann machines
V Nair, GE Hinton
Icml, 2010
139982010
Learning multiple layers of features from tiny images
A Krizhevsky, G Hinton
122412009
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
100722012
Speech recognition with deep recurrent neural networks
A Graves, A Mohamed, G Hinton
2013 IEEE international conference on acoustics, speech and signal …, 2013
77272013
Distilling the knowledge in a neural network
G Hinton, O Vinyals, J Dean
arXiv preprint arXiv:1503.02531, 2015
68312015
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
67362012
Training products of experts by minimizing contrastive divergence
GE Hinton
Neural computation 14 (8), 1771-1800, 2002
51852002
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
51022012
Adaptive mixtures of local experts
RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton
Neural computation 3 (1), 79-87, 1991
46931991
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Articles 1–20