Geoffrey Hinton
Geoffrey Hinton
Emeritus Prof. Computer Science, University of Toronto
Verificeret mail på cs.toronto.edu - Startside
Citeret af
Citeret af
Imagenet classification with deep convolutional neural networks
A Krizhevsky, I Sutskever, GE Hinton
Advances in neural information processing systems 25, 2012
Deep learning
Y LeCun, Y Bengio, G Hinton
Nature 521 (7553), 436-44, 2015
Learning internal representations by error-propagation
DE Rumelhart, GE Hinton, RJ Williams
Parallel Distributed Processing: Explorations in the Microstructure of …, 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
Visualizing data using t-SNE
L van der Maaten, G Hinton
Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008
Learning representations by back-propagating errors
DE Rumelhart, GE Hinton, RJ Williams
Nature 323 (6088), 533-536, 1986
Learning multiple layers of features from tiny images
A Krizhevsky, G Hinton
Rectified linear units improve restricted boltzmann machines
V Nair, GE Hinton
Proceedings of the 27th international conference on machine learning (ICML …, 2010
Reducing the dimensionality of data with neural networks
GE Hinton, RR Salakhutdinov
Science 313 (5786), 504-507, 2006
A fast learning algorithm for deep belief nets
GE Hinton, S Osindero, YW Teh
Neural computation 18 (7), 1527-1554, 2006
Distilling the knowledge in a neural network
G Hinton, O Vinyals, J Dean
arXiv preprint arXiv:1503.02531, 2015
A simple framework for contrastive learning of visual representations
T Chen, S Kornblith, M Norouzi, G Hinton
International conference on machine learning, 1597-1607, 2020
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
Layer normalization
JL Ba, JR Kiros, GE Hinton
arXiv preprint arXiv:1607.06450, 2016
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
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
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
Training products of experts by minimizing contrastive divergence
GE Hinton
Neural computation 14 (8), 1771-1800, 2002
On the importance of initialization and momentum in deep learning
I Sutskever, J Martens, G Dahl, G Hinton
International conference on machine learning, 1139-1147, 2013
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Artikler 1–20