Geoffrey Hinton
Geoffrey Hinton
Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google
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Imagenet classification with deep convolutional neural networks
A Krizhevsky, I Sutskever, GE Hinton
Advances in neural information processing systems 25, 1097-1105, 2012
Deep learning
Y LeCun, Y Bengio, G Hinton
Nature 521 (7553), 436-444, 2015
Learning internal representations by error-propagation
DE Rumelhart, GE Hinton, RJ Williams
Parallel Distributed Processing: Explorations in the Microstructure of …, 1986
Learning internal representations by error propagation
DE Rumelhart, GE Hinton, RJ Wlliams
Learning internal representations by error propagation, 1986
Learning internal representations by error propagation
DE Rumelhart, GE Hinton, RJ Williams
MIT Press, Cambridge, MA 1 (318), 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
Learning representations by back-propagating errors
DE Rumelhart, GE Hinton, RJ Williams
Nature 323 (6088), 533-536, 1986
Visualizing data using t-SNE
L van der Maaten, G Hinton
Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008
A fast learning algorithm for deep belief nets
GE Hinton, S Osindero, YW Teh
Neural computation 18 (7), 1527-1554, 2006
Reducing the dimensionality of data with neural networks
GE Hinton, RR Salakhutdinov
Science 313 (5786), 504-507, 2006
Rectified linear units improve restricted boltzmann machines
V Nair, GE Hinton
Icml, 2010
Learning multiple layers of features from tiny images
A Krizhevsky, G Hinton
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
Speech recognition with deep recurrent neural networks
A Graves, A Mohamed, G Hinton
2013 IEEE international conference on acoustics, speech and signal …, 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
Distilling the knowledge in a neural network
G Hinton, O Vinyals, J Dean
arXiv preprint arXiv:1503.02531, 2015
Training products of experts by minimizing contrastive divergence
GE Hinton
Neural computation 14 (8), 1771-1800, 2002
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
Adaptive mixtures of local experts
RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton
Neural computation 3 (1), 79-87, 1991
A learning algorithm for Boltzmann machines
DH Ackley, GE Hinton, TJ Sejnowski
Cognitive science 9 (1), 147-169, 1985
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