Geoffrey Hinton
Geoffrey Hinton
Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google
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Zitiert von
Zitiert von
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), 436-44, 2015
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 internal representations by error-propagation
DE Rumelhart, GE Hinton, RJ Williams
Parallel Distributed Processing: Explorations in the Microstructure of …, 1986
Learning representations by back-propagating errors
DE Rumelhart, GE Hinton, RJ Williams
Nature 323 (6088), 533-536, 1986
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
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
Distilling the knowledge in a neural network
G Hinton, O Vinyals, J Dean
arXiv preprint arXiv:1503.02531, 2015
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
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
Training products of experts by minimizing contrastive divergence
GE Hinton
Neural computation 14 (8), 1771-1800, 2002
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
Layer normalization
JL Ba, JR Kiros, GE Hinton
arXiv preprint arXiv:1607.06450, 2016
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