Shape representation in parallel systems GE Hinton Proceedings of the 7th international joint conference on Artificial ..., 1981 | 126 | 1981 |
Replicated softmax: an undirected topic model GE Hinton, RR Salakhutdinov Advances in neural information processing systems, 1607-1614, 2009 | 125 | 2009 |
Varieties of Helmholtz machine P Dayan, GE Hinton Neural Networks 9 (8), 1385-1403, 1996 | 124 | 1996 |
Learning representations by recirculation GE Hinton, JL McClelland Neural information processing systems, 358-366, 1988 | 122 | 1988 |
Adaptive elastic models for hand-printed character recognition GE Hinton, CKI Williams, MD Revow NIPS 4, 512-519, 1991 | 121 | 1991 |
Using expectation-maximization for reinforcement learning P Dayan, GE Hinton Neural Computation 9 (2), 271-278, 1997 | 120 | 1997 |
Recognizing handwritten digits using mixtures of linear models GE Hinton, M Revow, P Dayan Advances in neural information processing systems, 1015-1022, 1995 | 120 | 1995 |
Separating figure from ground with a parallel network PK Kienker, TJ Sejnowski, GE Hinton, LE Schumacher Perception 15 (2), 197-216, 1986 | 120 | 1986 |
Factored 3-way restricted boltzmann machines for modeling natural images A Krizhevsky, GE Hinton International Conference on Artificial Intelligence and Statistics, 621-628, 2010 | 119 | 2010 |
Learning sparse topographic representations with products of student-t distributions M Welling, S Osindero, GE Hinton Advances in neural information processing systems, 1359-1366, 2002 | 119 | 2002 |
Parallel computations for controlling an arm G Hinton Journal of motor behavior 16 (2), 171-194, 1984 | 115 | 1984 |
Energy-based models for sparse overcomplete representations YW Teh, M Welling, S Osindero, GE Hinton The Journal of Machine Learning Research 4, 1235-1260, 2003 | 112 | 2003 |
Learning symmetry groups with hidden units: Beyond the perceptron TJ Sejnowski, PK Kienker, GE Hinton Physica D: Nonlinear Phenomena 22 (1), 260-275, 1986 | 112 | 1986 |
Using fast weights to deblur old memories GE Hinton, DC Plaut Proceedings of the ninth annual conference of the Cognitive Science Society ..., 1987 | 111 | 1987 |
Frames of reference and mental imagery GE Hinton, LM Parsons Attention and performance IX, 261-277, 1981 | 111 | 1981 |
Evaluation of Adaptive Mixtures of Competing Experts. SJ Nowlan, GE Hinton NIPS 3, 774-780, 1990 | 110 | 1990 |
Learning sets of filters using back-propagation DC Plaut, GE Hinton Computer Speech & Language 2 (1), 35-61, 1987 | 110 | 1987 |
On rectified linear units for speech processing MD Zeiler, MA Ranzato, R Monga, M Mao, K Yang, QV Le, P Nguyen, ... Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International ..., 2013 | 108 | 2013 |
The recurrent temporal restricted boltzmann machine I Sutskever, GE Hinton, GW Taylor Advances in Neural Information Processing Systems, 1601-1608, 2009 | 108 | 2009 |
Scene-based and viewer-centered representations for comparing shapes GE Hinton, LM Parsons Cognition 30 (1), 1-35, 1988 | 107 | 1988 |
An efficient learning procedure for deep Boltzmann machines R Salakhutdinov, G Hinton Neural computation 24 (8), 1967-2006, 2012 | 103 | 2012 |
Binary coding of speech spectrograms using a deep auto-encoder. L Deng, ML Seltzer, D Yu, A Acero, A Mohamed, GE Hinton Interspeech, 1692-1695, 2010 | 101 | 2010 |
Learning multilevel distributed representations for high-dimensional sequences I Sutskever, GE Hinton International Conference on Artificial Intelligence and Statistics, 548-555, 2007 | 99 | 2007 |
Topographic product models applied to natural scene statistics S Osindero, M Welling, GE Hinton Neural Computation 18 (2), 381-414, 2006 | 98 | 2006 |
Representing part-whole hierarchies in connectionist networks GE Hinton Proceedings of the Tenth Annual Conference of the Cognitive Science Society ..., 1988 | 96 | 1988 |
Unsupervised learning of image transformations R Memisevic, G Hinton Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, 1-8, 2007 | 95 | 2007 |
A new learning algorithm for mean field Boltzmann machines M Welling, GE Hinton Artificial Neural Networks—ICANN 2002, 351-357, 2002 | 95 | 2002 |
On deep generative models with applications to recognition MA Ranzato, J Susskind, V Mnih, G Hinton Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on ..., 2011 | 94 | 2011 |
Learning to represent visual input GE Hinton Philosophical Transactions of the Royal Society B: Biological Sciences 365 ..., 2010 | 90 | 2010 |
New types of deep neural network learning for speech recognition and related applications: An overview L Deng, G Hinton, B Kingsbury Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International ..., 2013 | 88 | 2013 |
Understanding how deep belief networks perform acoustic modelling A Mohamed, G Hinton, G Penn Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International ..., 2012 | 88 | 2012 |
Relaxation and its role in vision GE Hinton University of Edinburgh, 1978 | 88 | 1978 |
Modeling image patches with a directed hierarchy of Markov random fields S Osindero, GE Hinton Advances in neural information processing systems, 1121-1128, 2008 | 87 | 2008 |
Simulating brain damage GE Hinton, DC Plaut, T Shallice Scientific American 268 (4), 76-82, 1993 | 84 | 1993 |
Shape recognition and illusory conjunctions. GE Hinton, KJ Lang IJCAI, 252-259, 1985 | 81 | 1985 |
Analysing cooperative computation GE Hinton, TJ Sejnowski Proceedings of the fifth annual conference of the cognitive science society, 1983 | 81 | 1983 |
Separating figure from ground with a Boltzmann machine TJ Sejnowski, GE Hinton In MA Arbib & A. Hanson (Eds.), Vision, brain, and, 1987 | 79 | 1987 |
In Rumelhart, DE, McClelland, JL and the PDP Research Group DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of ..., 1986 | 78 | 1986 |
Switching state-space models Z Ghahramani, GE Hinton Technical Report CRG-TR-96-3 DRAFT, Dept. of Computer Science, University of ..., 1996 | 75 | 1996 |
Learning population codes by minimizing description length RS Zemel, GE Hinton Neural computation 7 (3), 549-564, 1995 | 75 | 1995 |
Learning to detect roads in high-resolution aerial images V Mnih, GE Hinton Computer Vision–ECCV 2010, 210-223, 2010 | 74 | 2010 |
Reinforcement learning with factored states and actions B Sallans, GE Hinton The Journal of Machine Learning Research 5, 1063-1088, 2004 | 74 | 2004 |
Rate-coded restricted Boltzmann machines for face recognition YW Teh, GE Hinton Advances in neural information processing systems, 908-914, 2001 | 74 | 2001 |
A mobile robot that learns its place S Oore, GE Hinton, G Dudek Neural Computation 9 (3), 683-699, 1997 | 73 | 1997 |
Learning in parallel networks GE Hinton Byte 10 (4), 265-273, 1985 | 72 | 1985 |
Split and merge EM algorithm for improving Gaussian mixture density estimates N Ueda, R Nakano, Z Ghahramani, GE Hinton Journal of VLSI signal processing systems for signal, image and video ..., 2000 | 69 | 2000 |
Glove-TalkII: an adaptive gesture-to-formant interface S Fels, G Hinton Proceedings of the SIGCHI conference on Human factors in computing systems ..., 1995 | 69 | 1995 |
Using deep belief nets to learn covariance kernels for Gaussian processes GE Hinton, RR Salakhutdinov Advances in neural information processing systems, 1249-1256, 2008 | 68 | 2008 |
Dynamical binary latent variable models for 3d human pose tracking GW Taylor, L Sigal, DJ Fleet, GE Hinton Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 631-638, 2010 | 64 | 2010 |
Recognizing handwritten digits using hierarchical products of experts G Mayraz, GE Hinton Pattern Analysis and Machine Intelligence, IEEE Transactions on 24 (2), 189-197, 2002 | 64 | 2002 |
GTM through time CM Bishop, GE Hinton, IGD Strachan IET Digital Library, 1997 | 63 | 1997 |
Distinguishing text from graphics in on-line handwritten ink CM Bishop, M Svensen, GE Hinton null, 142-147, 2004 | 62 | 2004 |
A comparison of statistical learning methods on the GUSTO database M Ennis, G Hinton, D Naylor, M Revow, R Tibshirani Statistics in medicine 17 (21), 2501-2508, 1998 | 62 | 1998 |
Building adaptive interfaces with neural networks: The glove-talk pilot study S Fels, GE Hinton Proceedings of the IFIP TC13 Third Interational Conference on Human-Computer ..., 1990 | 62 | 1990 |
Deep learning Y LeCun, Y Bengio, G Hinton Nature 521 (7553), 436-444, 2015 | 59 | 2015 |
Automatic recognition of sheet music. DH Pruslin, G Read, D Prerau, WK Hastings, JV Mahoney, B Widrow, ... Biometrika} 21, 15-27, 1966 | 59 | 1966 |
A soft decision-directed LMS algorithm for blind equalization SJ Nowlan, GE Hinton IEEE Transactions on Communications 41 (2), 275-279, 1993 | 58 | 1993 |
The DELVE manual CE Rasmussen, RM Neal, GE Hinton, D van Camp, M Revow, ... URL http://www. cs. toronto. edu/~ delve, 1996 | 57 | 1996 |
The appeal of parallel distributed processing, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations JL McClelland, DE Rumelhart, GE Hinton MIT Press, Cambridge, MA, 1986 | 57 | 1986 |
Transforming auto-encoders GE Hinton, A Krizhevsky, SD Wang Artificial Neural Networks and Machine Learning–ICANN 2011, 44-51, 2011 | 56 | 2011 |
Learning a better representation of speech soundwaves using restricted boltzmann machines N Jaitly, G Hinton Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International ..., 2011 | 55 | 2011 |
Inferring motor programs from images of handwritten digits V Nair, GE Hinton Advances in neural information processing systems, 515-522, 2005 | 54 | 2005 |
Convolutional deep belief networks on cifar-10 A Krizhevsky, G Hinton Unpublished manuscript, 2010 | 53 | 2010 |
Implicit mixtures of restricted Boltzmann machines V Nair, GE Hinton Advances in neural information processing systems, 1145-1152, 2009 | 52 | 2009 |
Deep, narrow sigmoid belief networks are universal approximators I Sutskever, GE Hinton Neural Computation 20 (11), 2629-2636, 2008 | 52 | 2008 |
Learning to combine foveal glimpses with a third-order Boltzmann machine H Larochelle, G Hinton Advances in Neural Information Processing Systems 23 1, 2010 | 51 | 2010 |
Mean field networks that learn to discriminate temporally distorted strings CKI Williams, GE Hinton Connectionist models: Proceedings of the 1990 summer school, 18-22, 1991 | 50 | 1991 |
A desktop input device and interface for interactive 3d character animation S Oore, D Terzopoulos, G Hinton Graphics Interface 2, 133-140, 2002 | 49 | 2002 |
Does the wake-sleep algorithm produce good density estimators? BJ Frey, GE Hinton, P Dayan Advances in neural information processing systems, 661-670, 1996 | 49 | 1996 |
Learning mixture models of spatial coherence S Becker, GE Hinton Neural Computation 5 (2), 267-277, 1993 | 49 | 1993 |
Phone recognition using restricted boltzmann machines A Mohamed, G Hinton Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International ..., 2010 | 48 | 2010 |
Conditional Restricted Boltzmann Machines for Structured Output Prediction V Mnih, H Larochelle, GE Hinton Proc. Uncertainty in Artificial Intelligence, 2011 | 44 | 2011 |
Generating facial expressions with deep belief nets JM Susskind, AK Anderson, GE Hinton, JR Movellan INTECH Open Access Publisher, 2008 | 44 | 2008 |
Using very deep autoencoders for content-based image retrieval. A Krizhevsky, GE Hinton ESANN, 2011 | 43 | 2011 |
G-maximization: An unsupervised learning procedure for discovering regularities BA Pearlmutter, G Hinton AIP conference proceedings 151, 333-338, 1986 | 43 | 1986 |
Generating more realistic images using gated mrf’s M Ranzato, V Mnih, GE Hinton NIPS 2858, 2859-2860, 2010 | 42* | 2010 |
Learning Internal Representations by Error Propagation, Parallel Distributed Processing, Explorations in the Microstructure of Cognition, ed. DE Rumelhart and J. McClelland. Vol. 1. 1986 DE Rumelhart, GE Hinton, RJ Williams Cambridge, MA: MIT Press, 1986 | 41 | 1986 |
Generative versus discriminative training of RBMs for classification of fMRI images T Schmah, GE Hinton, SL Small, S Strother, RS Zemel Advances in neural information processing systems, 1409-1416, 2008 | 39 | 2008 |
Variational learning in nonlinear Gaussian belief networks BJ Frey, GE Hinton Neural Computation 11 (1), 193-213, 1999 | 39 | 1999 |
A hierarchical community of experts GE Hinton, B Sallans, Z Ghahramani Learning in graphical models, 479-494, 1998 | 39* | 1998 |
Discovering viewpoint-invariant relationships that characterize objects RS Zemel, GE Hinton Advances in neural information processing systems, 299-305, 1991 | 39 | 1991 |
h Williams, R.(1986). Learning internal representations by error propagation D Rumelhart, G Hinton DE Rumelhan 8, 6 | 39* | 6 |
Robust boltzmann machines for recognition and denoising Y Tang, R Salakhutdinov, G Hinton Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on ..., 2012 | 38 | 2012 |
Two distributed-state models for generating high-dimensional time series GW Taylor, GE Hinton, ST Roweis The Journal of Machine Learning Research 12, 1025-1068, 2011 | 38 | 2011 |
Discovering binary codes for documents by learning deep generative models G Hinton, R Salakhutdinov Topics in Cognitive Science 3 (1), 74-91, 2011 | 38 | 2011 |
Learning internal representation by back propagation DE Rumelhart, GE Hinton, RJ Williams Parallel distributed processing: exploration in the microstructure of ..., 1986 | 38 | 1986 |
Deep belief networks GE Hinton Scholarpedia 4 (5), 5947, 2009 | 37 | 2009 |
A modified gating network for the mixtures of experts architecture L Xu, MI Jordan, GE Hinton World Congress on Neural Networks II, San Diego CA, June, 405-410, 1994 | 37 | 1994 |
McClelland. JL (1986) DE Rumelhart, GE Hinton Parallel Distributed Processing, Explorations in the Microstructure of ..., 81 | 37 | 81 |
A better way to pretrain deep Boltzmann machines GE Hinton, RR Salakhutdinov Advances in Neural Information Processing Systems, 2447-2455, 2012 | 36 | 2012 |
Boltzmann machines G Hinton Encyclopedia of Machine Learning, 132-136, 2010 | 36 | 2010 |
Products of hidden markov models A Brown, GE Hinton Proceedings of Artificial Intelligence and Statistics, 3-11, 2001 | 36 | 2001 |
Evaluating the interface of a document processor: a comparison of expert judgement and user observation N Hammond, G Hinton, P Barnard, A MacLean, J Long, A Whitefield Human-Computer Interaction-INTERACT 84, 1984 | 36 | 1984 |
Distilling the knowledge in a neural network G Hinton, O Vinyals, J Dean arXiv preprint arXiv:1503.02531, 2015 | 35 | 2015 |
Visualizing similarity data with a mixture of maps J Cook, I Sutskever, A Mnih, GE Hinton International Conference on Artificial Intelligence and Statistics, 67-74, 2007 | 35 | 2007 |
Spatial Coherence as an Internal Teacher for a Neural Network GE Hinton Backpropagation: theory, architectures, and applications, 313, 1995 | 35* | 1995 |
Adaptive soft weight tying using gaussian mixtures SJ Nowlan, GE Hinton Advances in Neural Information Processing Systems, 993-1000, 1992 | 35 | 1992 |
Modeling documents with deep boltzmann machines N Srivastava, RR Salakhutdinov, GE Hinton arXiv preprint arXiv:1309.6865, 2013 | 33 | 2013 |
Modeling the joint density of two images under a variety of transformations J Susskind, R Memisevic, G Hinton, M Pollefeys Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on ..., 2011 | 33 | 2011 |
Solving random-dot stereograms using the heat equation R Szeliski, G Hinton CVPR, 1985 | 32 | 1985 |