Parallel distributed processing DE Rumelhart, JL McClelland, PDP Research Group IEEE 1, 354-362, 1988 | 18211 | 1988 |
Learning internal representations by error-propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of ..., 1986 | 17876 | 1986 |
Learning internal representations by error propagation DE Rumelhart, GE Hinton, RJ Williams CALIFORNIA UNIV SAN DIEGO LA JOLLA INST FOR, 1985 | 17870 | 1985 |
Learning representations by back-propagating errors DE Rumelhart, GE Hinton, RJ Williams Nature 323, 533-536, 1986 | 9327 | 1986 |
Parallel distributed processing JL McClelland, DE Rumelhart, PDP Research Group Explorations in the microstructure of cognition 2, 1986 | 3533* | 1986 |
A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006 | 3170 | 2006 |
Adaptive mixtures of local experts RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton Neural computation 3 (1), 79-87, 1991 | 3089 | 1991 |
A learning algorithm for boltzmann machines* DH Ackley, GE Hinton, TJ Sejnowski Cognitive science 9 (1), 147-169, 1985 | 2697 | 1985 |
Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems, 1097-1105, 2012 | 2674 | 2012 |
Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov Science 313 (5786), 504-507, 2006 | 2622 | 2006 |
A view of the EM algorithm that justifies incremental, sparse, and other variants RM Neal, GE Hinton Learning in graphical models, 355-368, 1998 | 2104 | 1998 |
Phoneme recognition using time-delay neural networks A Waibel, T Hanazawa, G Hinton, K Shikano, KJ Lang Acoustics, Speech and Signal Processing, IEEE Transactions on 37 (3), 328-339, 1989 | 1934 | 1989 |
Training products of experts by minimizing contrastive divergence GE Hinton Neural computation 14 (8), 1771-1800, 2002 | 1803 | 2002 |
Connectionist learning procedures GE Hinton Artificial intelligence 40 (1), 185-234, 1989 | 1490 | 1989 |
Learning and relearning in Boltzmann machines GE Hinton, TJ Sejnowski Parallel distributed processing: Explorations in the microstructure of ..., 1986 | 1397* | 1986 |
Distributed representations GE Hinton, JL McClelland, DE Rumelhart Parallel distributed processing: Explorations in the microstructure of ..., 1986 | 1330 | 1986 |
How learning can guide evolution GE Hinton, SJ Nowlan Complex systems 1 (3), 495-502, 1987 | 1145 | 1987 |
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 | 1090* | 1986 |
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, ... Signal Processing Magazine, IEEE 29 (6), 82-97, 2012 | 1045 | 2012 |
The appeal of parallel distributed processing. JL McClelland, DE Rumelhart, GE Hinton Parallel distributed processing: Explorations in the microstructure of ..., 1986 | 994 | 1986 |
Visualizing data using t-SNE L van der Maaten, G Hinton Journal of Machine Learning Research 9 (Nov), 2579-2605, 2008 | 969 | 2008 |
Parallel models of associative memory GE Hinton, JA Anderson Lawrence Erlbaum Associates, 1981 | 905* | 1981 |
Neighbourhood components analysis J Goldberger, GE Hinton, ST Roweis, R Salakhutdinov Advances in neural information processing systems, 513-520, 2004 | 874 | 2004 |
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 | 800 | 2012 |
Lesioning an attractor network: investigations of acquired dyslexia. GE Hinton, T Shallice Psychological review 98 (1), 74, 1991 | 695 | 1991 |
The" wake-sleep" algorithm for unsupervised neural networks GE Hinton, P Dayan, BJ Frey, RM Neal Science 268 (5214), 1158-1161, 1995 | 659 | 1995 |
The helmholtz machine P Dayan, GE Hinton, RM Neal, RS Zemel Neural computation 7 (5), 889-904, 1995 | 623 | 1995 |
The EM algorithm for mixtures of factor analyzers Z Ghahramani, GE Hinton Technical Report CRG-TR-96-1, University of Toronto, 1996 | 620 | 1996 |
Learning distributed representations of concepts GE Hinton Proceedings of the eighth annual conference of the cognitive science society ..., 1986 | 594 | 1986 |
A practical guide to training restricted boltzmann machines G Hinton Momentum, 1, 2010 | 554 | 2010 |
Learning multiple layers of features from tiny images A Krizhevsky, G Hinton Technical report, University of Toronto, 2009 | 544 | 2009 |
Boltzmann machines: Constraint satisfaction networks that learn GE Hinton, TJ Sejnowski, DH Ackley Carnegie-Mellon University, Department of Computer Science, 1984 | 522 | 1984 |
Acoustic modeling using deep belief networks A Mohamed, G Dahl, G Hinton Audio, Speech, and Language Processing, IEEE Transactions on 20, 14-22, 2012 | 519 | 2012 |
Restricted Boltzmann machines for collaborative filtering R Salakhutdinov, A Mnih, G Hinton Proceedings of the 24th international conference on Machine learning, 791-798, 2007 | 507 | 2007 |
A time-delay neural network architecture for isolated word recognition KJ Lang, AH Waibel, GE Hinton Neural networks 3 (1), 23-43, 1990 | 497 | 1990 |
Stochastic neighbor embedding GE Hinton, ST Roweis Advances in neural information processing systems, 833-840, 2002 | 494 | 2002 |
Deep Boltzmann machines R Salakhutdinov, G Hinton Artificial Intelligence and Statistics, 2009 | 486 | 2009 |
Classical and Bayesian inference in neuroimaging: theory KJ Friston, W Penny, C Phillips, S Kiebel, G Hinton, J Ashburner NeuroImage 16 (2), 465-483, 2002 | 470 | 2002 |
How neural networks learn from experience GE Hinton Scientific American 267 (3), 145-151, 1992 | 453 | 1992 |
Rectified linear units improve restricted boltzmann machines V Nair, GE Hinton Proceedings of the 27th International Conference on Machine Learning (ICML ..., 2010 | 438 | 2010 |
Optimal perceptual inference GE Hinton, TJ Sejnowski Proceedings of the IEEE conference on Computer Vision and Pattern ..., 1983 | 426 | 1983 |
Learning representations by back-propagating errors DERGEHRJ Williams, GE Hinton Nature, 323,533-538, 1986 | 385 | 1986 |
Parallel visual computation DH Ballard, GE Hinton, TJ Sejnowski Nature 306 (5938), 21-26, 1983 | 384 | 1983 |
Learning multiple layers of representation GE Hinton Trends in cognitive sciences 11 (10), 428-434, 2007 | 375 | 2007 |
Parameter estimation for linear dynamical systems Z Ghahramani, GE Hinton Technical Report CRG-TR-96-2, University of Totronto, Dept. of Computer Science, 1996 | 373 | 1996 |
Simplifying neural networks by soft weight-sharing SJ Nowlan, GE Hinton Neural computation 4 (4), 473-493, 1992 | 373 | 1992 |
Semantic hashing R Salakhutdinov, G Hinton International Journal of Approximate Reasoning 50 (7), 969-978, 2009 | 372 | 2009 |
SMEM algorithm for mixture models N Ueda, R Nakano, Z Ghahramani, GE Hinton Neural computation 12 (9), 2109-2128, 2000 | 367 | 2000 |
Modeling the manifolds of images of handwritten digits GE Hinton, P Dayan, M Revow Neural Networks, IEEE Transactions on 8 (1), 65-74, 1997 | 362 | 1997 |
Keeping the neural networks simple by minimizing the description length of the weights GE Hinton, D Van Camp Proceedings of the sixth annual conference on Computational learning theory ..., 1993 | 355 | 1993 |
Experiments on Learning by Back Propagation. DC Plaut | 355 | 1986 |
Glove-talk: A neural network interface between a data-glove and a speech synthesizer SS Fels, GE Hinton Neural Networks, IEEE Transactions on 4 (1), 2-8, 1993 | 342 | 1993 |
Variational learning for switching state-space models Z Ghahramani, GE Hinton Neural computation 12 (4), 831-864, 2000 | 340 | 2000 |
A distributed connectionist production system DS Touretzky, GE Hinton Cognitive Science 12 (3), 423-466, 1988 | 339 | 1988 |
Modeling human motion using binary latent variables GW Taylor, GE Hinton, ST Roweis Advances in neural information processing systems, 1345-1352, 2006 | 330 | 2006 |
Feudal reinforcement learning P Dayan, GE Hinton Advances in neural information processing systems, 271-271, 1993 | 329 | 1993 |
Self-organizing neural network that discovers surfaces in random-dot stereograms S Becker, GE Hinton Nature 355 (6356), 161-163, 1992 | 323 | 1992 |
Implementing semantic networks in parallel hardware GE Hinton Parallel models of associative memory, 161-187, 1981 | 315 | 1981 |
Products of experts GE Hinton Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference ..., 1999 | 301 | 1999 |
Deep belief networks using discriminative features for phone recognition A Mohamed, TN Sainath, G Dahl, B Ramabhadran, GE Hinton, M Picheny Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International ..., 2011 | 297 | 2011 |
Exponential family harmoniums with an application to information retrieval M Welling, M Rosen-Zvi, GE Hinton Advances in neural information processing systems, 1481-1488, 2004 | 282 | 2004 |
Speech recognition with deep recurrent neural networks A Graves, A Mohamed, G Hinton Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International ..., 2013 | 281 | 2013 |
On contrastive divergence learning MA Carreira-Perpinan, GE Hinton Proceedings of the tenth international workshop on artificial intelligence ..., 2005 | 281 | 2005 |
Mapping part-whole hierarchies into connectionist networks GE Hinton Artificial Intelligence 46 (1), 47-75, 1990 | 271 | 1990 |
A scalable hierarchical distributed language model A Mnih, GE Hinton Advances in neural information processing systems, 1081-1088, 2009 | 268 | 2009 |
Autoencoders, minimum description length, and Helmholtz free energy GE Hinton, RS Zemel Advances in neural information processing systems, 3-3, 1994 | 266 | 1994 |
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 | 251 | 2014 |
Generative models for discovering sparse distributed representations GE Hinton, Z Ghahramani Philosophical Transactions of the Royal Society B: Biological Sciences 352 ..., 1997 | 250 | 1997 |
Neuroanimator: Fast neural network emulation and control of physics-based models R Grzeszczuk, D Terzopoulos, G Hinton Proceedings of the 25th annual conference on Computer graphics and ..., 1998 | 249 | 1998 |
Models of information processing in the brain JA Anderson, GE Hinton Parallel models of associative memory, 9-48, 1981 | 233 | 1981 |
An alternative model for mixtures of experts L Xu, MI Jordan, GE Hinton Advances in neural information processing systems, 633-640, 1995 | 225 | 1995 |
Unsupervised learning: foundations of neural computation GE Hinton, TJ Sejnowski The MIT Press, 1999 | 215 | 1999 |
Learning a nonlinear embedding by preserving class neighbourhood structure R Salakhutdinov, GE Hinton International Conference on Artificial Intelligence and Statistics, 412-419, 2007 | 214 | 2007 |
Global coordination of local linear models ST Roweis, LK Saul, GE Hinton Advances in neural information processing systems 2, 889-896, 2002 | 204 | 2002 |
MASSIVELY PARALLEL ARCHITECTURES FOR Al: NETL, THISTLE, AND BOLTZMANN MACHINES SE Fahlman, GE Hinton, TJ Sejnowski | 193 | 1983 |
A parallel computation that assigns canonical object-based frames of reference GE Hinton Proceedings of the 7th international joint conference on Artificial ..., 1981 | 193 | 1981 |
Three new graphical models for statistical language modelling A Mnih, G Hinton Proceedings of the 24th international conference on Machine learning, 641-648, 2007 | 190 | 2007 |
Symbols among the neurons: Details of a connectionist inference architecture DS Touretzky, GE Hinton IJCAI 85, 238-243, 1985 | 189 | 1985 |
To recognize shapes, first learn to generate images GE Hinton Computational Neuroscience: Theoretical Insights into Brain Function, 535-547, 2007 | 181 | 2007 |
Deterministic Boltzmann learning performs steepest descent in weight-space GE Hinton Neural computation 1 (1), 143-150, 1989 | 178 | 1989 |
Connectionist architectures for artificial intelligence SE Fahlman, GE Hinton IEEE Computer 20, 100-109, 1987 | 171* | 1987 |
Learning translation invariant recognition in a massively parallel networks GE Hinton PARLE Parallel Architectures and Languages Europe, 1-13, 1987 | 169 | 1987 |
Improving deep neural networks for LVCSR using rectified linear units and dropout GE Dahl, TN Sainath, GE Hinton Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International ..., 2013 | 167 | 2013 |
On the importance of initialization and momentum in deep learning I Sutskever, J Martens, G Dahl, G Hinton Proceedings of the 30th international conference on machine learning (ICML ..., 2013 | 162 | 2013 |
A theoretical framework for back-propagation Y Le Cun, D Touresky, G Hinton, T Sejnowski The Connectionist Models Summer School 1, 21-28, 1988 | 160 | 1988 |
3D object recognition with deep belief nets V Nair, GE Hinton Advances in Neural Information Processing Systems, 1339-1347, 2009 | 159 | 2009 |
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls SS Fels, GE Hinton Neural Networks, IEEE Transactions on 9 (1), 205-212, 1998 | 158 | 1998 |
Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models A Waibel, T Hanazawa, G Hinton, K Shikano, K Lang Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 ..., 1988 | 158 | 1988 |
Connectionist symbol processing GE Hinton | 155* | 1990 |
Zero-shot learning with semantic output codes M Palatucci, D Pomerleau, GE Hinton, TM Mitchell Advances in neural information processing systems, 1410-1418, 2009 | 153 | 2009 |
Semantic hashing R Salakhutdinov, G Hinton SIGIR Workshop on Information Retrieval and Applications of Graphical Models ..., 2007 | 153 | 2007 |
Some demonstrations of the effects of structural descriptions in mental imagery G Hinton Cognitive Science 3 (3), 231-250, 1979 | 151 | 1979 |
Generating text with recurrent neural networks I Sutskever, J Martens, GE Hinton Proceedings of the 28th International Conference on Machine Learning (ICML ..., 2011 | 145 | 2011 |
Factored conditional restricted Boltzmann machines for modeling motion style GW Taylor, GE Hinton Proceedings of the 26th annual international conference on machine learning ..., 2009 | 145 | 2009 |
Using generative models for handwritten digit recognition M Revow, CKI Williams, GE Hinton Pattern Analysis and Machine Intelligence, IEEE Transactions on 18 (6), 592-606, 1996 | 142 | 1996 |
A practical guide to training restricted boltzmann machines GE Hinton Neural Networks: Tricks of the Trade, 599-619, 2012 | 141 | 2012 |
Modeling pixel means and covariances using factorized third-order Boltzmann machines MA Ranzato, GE Hinton Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on ..., 2010 | 141 | 2010 |
Using fast weights to improve persistent contrastive divergence T Tieleman, G Hinton Proceedings of the 26th Annual International Conference on Machine Learning ..., 2009 | 138 | 2009 |
Phone recognition with the mean-covariance restricted Boltzmann machine G Dahl, A Mohamed, GE Hinton Advances in neural information processing systems, 469-477, 2010 | 128 | 2010 |
Learning to represent spatial transformations with factored higher-order Boltzmann machines R Memisevic, GE Hinton Neural Computation 22 (6), 1473-1492, 2010 | 126 | 2010 |