| Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 1097-1105, 2012 | 84542 | 2012 |
| Deep learning Y LeCun, Y Bengio, G Hinton Nature 521 (7553), 436-444, 2015 | 37925 | 2015 |
| Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of …, 1986 | 27754 | 1986 |
| Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Wlliams Learning internal representations by error propagation, 1986 | 27687 | 1986 |
| Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA 1 (318), 1986 | 27490 | 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 | 27364 | 2014 |
| Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323 (6088), 533-536, 1986 | 24866 | 1986 |
| Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008 | 19303 | 2008 |
| A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006 | 15216 | 2006 |
| Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov Science 313 (5786), 504-507, 2006 | 14861 | 2006 |
| Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Icml, 2010 | 13016 | 2010 |
| Learning multiple layers of features from tiny images A Krizhevsky, G Hinton | 11027 | 2009 |
| 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 | 9645 | 2012 |
| Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton 2013 IEEE international conference on acoustics, speech and signal …, 2013 | 7295 | 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 | 6355 | 2012 |
| Distilling the knowledge in a neural network G Hinton, O Vinyals, J Dean arXiv preprint arXiv:1503.02531, 2015 | 5922 | 2015 |
| Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002 | 5040 | 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 | 4884 | 2012 |
| Adaptive mixtures of local experts RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton Neural computation 3 (1), 79-87, 1991 | 4525 | 1991 |
| A learning algorithm for Boltzmann machines DH Ackley, GE Hinton, TJ Sejnowski Cognitive science 9 (1), 147-169, 1985 | 4457 | 1985 |
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoVerified email at salk.edu
