Geoffrey E. Hinton's Publications
in Reverse Chronological Order

2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996  1995  1994  1993  1992  1991  1990  1989  1988  1987  1986  1985  1984  1983-1976

2014 Sarikaya, R., Hinton, G. E. and Deoras, A.
Application of Deep Belief Networks for Natural Language Understanding
IEEE Transactions on Audio, Speech and Language Processing. [pdf]
2013 Srivastava, N., Salakhutdinov, R. R. and Hinton, G. E.
Modeling Documents with a Deep Boltzmann Machine
In Uncertainty in Artificial Intelligence (UAI 2013) [pdf]
2013 Graves, A., Mohamed, A. and Hinton, G. E.
Speech Recognition with Deep Recurrent Neural Networks
In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf]
2013 Dahl, G. E., Sainath, T. N. and Hinton, G. E.
Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout
In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf]
2013 M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G. Hinton
On Rectified Linear Units for Speech Processing
In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf]
2013 Deng, L., Hinton, G. E. and Kingsbury, B.
New types of deep neural network learning for speech recognition and related applications: An overview
In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf]
2013 Ranzato, M., Mnih, V., Susskind, J. and Hinton, G. E.
Modeling Natural Images Using Gated MRFs
IEEE Trans. Pattern Analysis and Machine Intelligence, to appear [pdf]
2013 Sutskever, I., Martens, J., Dahl, G. and Hinton, G. E.
On the importance of momentum and initialization in deep learning
In 30th International Conference on Machine Learning, Atlanta, USA [pdf]
2013 Tang, Y., Salakhutdinov, R. R. and Hinton, G. E.
Tensor Analyzers
In 30th International Conference on Machine Learning, Atlanta, USA [pdf]
2012 Krizhevsky, A., Sutskever, I. and Hinton, G. E.
ImageNet Classification with Deep Convolutional Neural Networks
Advances in Neural Information Processing 25, MIT Press, Cambridge, MA [pdf]
2012 Salakhutdinov, R. R. and Hinton, G. E.
A Better Way to Pretrain Deep Boltzmann Machines
Advances in Neural Information Processing 25, MIT Press, Cambridge, MA [pdf]
2012 Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. R.
Improving neural networks by preventing co-adaptation of feature detectors
http://arxiv.org/abs/1207.0580 [pdf]
2012 Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and Brian Kingsbury
Deep Neural Networks for Acoustic Modeling in Speech Recognition
IEEE Signal Processing Magazine, November 2012 (in press) [pdf]
2012 Salakhutdinov, R. R. and Hinton, G. E.
An Efficient Learning Procedure for Deep Boltzmann Machines
Neural Computation [pdf]
2012 Tang, Y., Salakhutdinov, R. R. and Hinton, G. E.
Deep Lambertian Networks
International Conference on Machine Learning, [pdf]
2012 Mnih, V. and Hinton, G. E.
Learning to Label Aerial Images from Noisy Data
International Conference on Machine Learning, [pdf]
2012 Tang, Y., Salakhutdinov, R. R. and Hinton, G. E.
Deep Mixtures of Factor Analysers
International Conference on Machine Learning, [pdf]
2012 Tang, Y., Salakhutdinov, R. R. and Hinton, G. E.
Robust Boltzmann Machines for Recognition and Denoising
IEEE Conference on Computer Vision and Pattern Recognition, [pdf]
2012 Mohamed,A., Hinton, G. E. and Penn, G.
Understanding how Deep Belief Networks perform acoustic modelling
ICASSP 2012, Kyoto. [pdf]
2012 van der Maaten, L., and Hinton, G. E.
Visualizing non-metric similarities in multiple maps
Machine Learning, Vol. 86 [pdf]
2012 Mohamed, A., Dahl, G. E. and Hinton, G. E.
Acoustic Modeling using Deep Belief Networks.
IEEE Trans. on Audio, Speech, and Language Processing (in press) [pdf]
2011 Suskever, I., Martens, J. and Hinton, G. E.
Generating Text with Recurrent Neural Networks.
Proc. 28th International Conference on Machine Learning, Seattle. [pdf]
2011 Jaitly, N. and Hinton, G.
A new way to learn acoustic events
Advances in Neural Information Processing Systems 24, Deep Learning workshop. [pdf]
2011 Mnih, V., Larochelle, H. and Hinton, G.
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Proc. Uncertainty in Artificial Intelligence. [pdf]
2011 Ranzato, M., Susskind, J., Mnih, V. and Hinton, G.
On deep generative models with applications to recognition.
IEEE Conference on Computer Vision and Pattern Recognition. [pdf]
2011 Susskind,J., Memisevic, R., Hinton, G. and Pollefeys, M.
Modeling the joint density of two images under a variety of transformations.
IEEE Conference on Computer Vision and Pattern Recognition [pdf]
2011 Hinton, G. E., Krizhevsky, A. and Wang, S.
Transforming Auto-encoders.,
ICANN-11: International Conference on Artificial Neural Networks, Helsinki. [pdf]
2011 Krizhevsky, A. and Hinton, G.E.
Using Very Deep Autoencoders for Content-Based Image Retrieval.
European Symposium on Artificial Neural Networks ESANN-2011, Bruges, Belgium. [pdf]
2011 Jaitly, N. and Hinton, G. E.
Learning a better Representation of Speech Sound Waves using Restricted Boltzmann Machines.
ICASSP-2011 [pdf]
2011 Mohamed,A., Sainath, T., Dahl, G. E., Ramabhadran, B., Hinton, G. and Picheny, M.
Deep Belief Networks using Discriminative Features for Phone Recognition.
ICASSP-2011 [pdf]
2011 Sarikaya, R. and Hinton, G.
Deep Belief Nets for Natural Language Call-Routing.
ICASSP-2011 [pdf]
2011 Hinton, G. E. and Salakhutdinov, R.
Discovering Binary Codes for Fast Document Retrieval by Learning Deep Generative Models.
Topics in Cognitive Science, Vol 3, pp 74-91. [pdf coming soon]
2010 Memisevic, R., Zach, C., Pollefeys, M. and Hinton, G. E.
Gated Softmax Classification.
Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf]
2010 Ranzato, M., Mnih, V. and Hinton, G. E.
Generating more realistic images using gated MRF's.
Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf]
2010 Dahl, G. E., Ranzato, M., Mohamed, A. and Hinton, G. E.
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine.
Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf]
2010 Larochelle, H. and Hinton, G. E.
Learning to combine foveal glimpses with a third-order Boltzmann machine.
Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf]
2010 Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed A. and Hinton, G.
Binary Coding of Speech Spectrograms Using a Deep Auto-encoder.
Interspeech 2010, Makuhari, Chiba, Japan. [pdf]
2010 Memisevic, R. and Hinton, G. E.
Learning to represent spatial transformations with factored higher-order Boltzmann machines.
Neural Computation, Vol 22, pp 1473-1492. [pdf]
2010 Nair, V. and Hinton, G. E.
Rectified linear units improve restricted Boltzmann machines.
Proc. 27th International Conference on Machine Learning [pdf]
2010 Hinton, G. E.
Learning to represent visual input.
Philosophical Transactions of the Royal Society, B. Vol 365, pp 177-184. [pdf]
2010 Mnih, V. and Hinton, G. E.
Learning to detect roads in high-resolution aerial images.
European Conference on Computer Vision. [pdf]
2010 Sutskever, I. and Hinton, G. E.
Temporal Kernel Recurrent Neural Networks.
Neural Networks, Vol 23, pp 239-243. [online text]
2010 Ranzato, M. and Hinton, G. E.
Modeling pixel means and covariances using factored third-order Boltzmann machines.
IEEE Conference on Computer Vision and Pattern Recognition. [pdf]
2010 Taylor, G., Sigal, L., Fleet, D. and Hinton, G. E.
Dynamic binary latent variable models for 3D human pose tracking.
IEEE Conference on Computer Vision and Pattern Recognition. [pdf]
2010 Ranzato, M., Krizhevsky, A. and Hinton, G. E.
Factored 3-way restricted Boltzmann machines for modeling natural images.
Proc. Thirteenth International Conference on Artificial Intelligence and Statistics. [pdf]
2010 Mohamed, A. and Hinton, G. E.
Phone Recognition using Restricted Boltzmann Machines.
To appear in ICASSP-10, Texas. [pdf]
2009 Mohamed, A. R., Dahl, G. E. and Hinton, G. E.
Deep belief networks for phone recognition.
NIPS 22 workshop on deep learning for speech recognition. [pdf]
2009 Salakhutdinov, R. and Hinton, G. E.
Replicated Softmax: An Undirected Topic Model.
Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.), pp 1607-1614. [pdf]
2009 Nair, V. and Hinton, G. E.
3-D Object recognition with deep belief nets.
Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.), pp 1339-1347. [pdf]
2009 Palatucci, M, Pomerleau, D. A., Hinton, G. E. and Mitchell, T.
Zero-Shot Learning with Semantic Output Codes.
Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.), pp 1410-1418. [pdf]
2009 Heess, N., Williams, C. K. I. and Hinton, G. E.
Learning generative texture models with extended Fields-of-Experts.
Proc. British Machine Vision Conf. [pdf]
2009 Taylor, G. W. and Hinton, G. E.
Products of Hidden Markov Models: It Takes N>1 to Tango.
Proc. of the 25th Conference on Uncertainty in Artificial Intelligence. [pdf]
2009 Taylor, G. W. and Hinton, G. E.
Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style.
Proc. 26th International Conference on Machine Learning}, pp 1025-1032. Omnipress, Montreal, Quebec. [pdf]
2009 Tieleman, T. and Hinton, G. E.
Using Fast Weights to Improve Persistent Contrastive Divergence.
Proc. 26th International Conference on Machine Learning, pp 1033-1040. Omnipress, Montreal, Quebec. [pdf]
2009 Zeiler, M.D., Taylor, G.W., Troje, N.F. and Hinton, G. E.
Modeling pigeon behaviour using a Conditional Restricted Boltzmann Machine.
European Symposium on Artificial Neural Networks ESANN-2009. [pdf]
2009 Salakhutdinov, R. and Hinton, G. E.
Deep Boltzmann Machines.
To appear in Artificial Intelligence and Statistics 2009
[pdf]
2009 Mnih, A. and Hinton, G.~E.
A Scalable Hierarchical Distributed Language Model.
Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA
[pdf]
2009 Nair, V. and Hinton, G.~E.
Implicit Mixtures of Restricted Boltzmann Machines.
Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA
[pdf]
2009 Sutskever, I. and Hinton, G.~E.
Using matrices to model symbolic relationships.
Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA
[pdf]
2009 Sutskever, I., Hinton, G.~E. and Taylor, G. W.
The Recurrent Temporal Restricted Boltzmann Machine.
Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA
[pdf]
2009 Schmah, T., Hinton, G.~E., Zemel, R., Small, S. and Strother, S.
Generative versus Discriminative Training of RBM's for classification of fMRI images.
Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA
[pdf]
2008 van der Maaten, L. J. P. and Hinton, G. E.
Visualizing Data using t-SNE.
Journal of Machine Learning Research, Vol 9, (Nov) pp 2579-2605.
[pdf] [supplementary material in pdf (25MB)]
2008 Susskind, J.M., Hinton, G.~E., Movellan, J.R., and Anderson, A.K.
Generating Facial Expressions with Deep Belief Nets.
In V. Kordic (ed.) Affective Computing, Emotion Modelling, Synthesis and Recognition. ARS Publishers.
[pdf]
2008 Nair, V., Susskind, J., and Hinton, G.E.
Analysis-by-Synthesis by Learning to Invert Generative Black Boxes.
ICANN-08: International conference on Artificial Neural Networks, Prague.
[pdf]
2008 Yuecheng, Z., Mnih, A., and Hinton, G.~E.
Improving a statistical language model by modulating the effects of context words.
16th European Symposium on Artificial Neural Networks, pages 493--498.
[pdf]
2008 Sutskever, I. and Hinton, G. E.
Deep Narrow Sigmoid Belief Networks are Universal Approximators.
Neural Computation, Vol 20, pp 2629-2636. [pdf]
2008 Osindero, S. and Hinton, G.~E.
Modeling image patches with a directed hierarchy of Markov random fields.
Advances in Neural Information Processing Systems 20,
J.C. Platt and D. Koller and Y. Singer and S. Roweis (eds.), MIT Press, Cambridge, MA [pdf]
2008 Salakhutdinov, R. and Hinton, G.~E.
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
Advances in Neural Information Processing Systems 20,
J.C. Platt and D. Koller and Y. Singer and S. Roweis (eds.), MIT Press, Cambridge, MA [pdf]
2007 Hinton, G. E.
Learning multiple layers of representation.
Trends in Cognitive Sciences, Vol. 11, pp 428-434.
[pdf]
2007 Hinton, G.~E.
To recognize shapes, first learn to generate images.
In P. Cisek, T. Drew and J. Kalaska (Eds.)
Computational Neuroscience: Theoretical Insights into Brain Function. Elsevier. [pdf of final draft]
2007 Hinton, G.~E. (2007)
Boltzmann machine. Scholarpedia, 2(5):1668
2007 Salakhutdinov R. R, Mnih, A. and Hinton, G. E.
Restricted Boltzmann Machines for Collaborative Filtering
International Conference on Machine Learning, Corvallis, Oregon. [pdf]
2007 Mnih, A. and Hinton, G. E.
Three New Graphical Models for Statistical Language Modelling
International Conference on Machine Learning, Corvallis, Oregon. [pdf]
2007 Salakhutdinov R. R, and Hinton, G. E.
Semantic Hashing.
Proceedings of the SIGIR Workshop on Information Retrieval and Applications of Graphical Models, Amsterdam. [pdf]
2007 Memisevic, R. and Hinton, G. E.
Unsupervised learning of image transformations.
Computer Vision and Pattern Recognition (CVPR-07). [pdf
A longer version: Technical Report UTML TR 2006-005. [pdf]
2007 Salakhutdinov R. R, and Hinton, G. E.
Learning a non-linear embedding by preserving class neighbourhood structure.
AI and Statistics, 2007, Puerto Rico. [pdf]
2007 Sutskever, I. and Hinton, G. E.
Learning Multilevel Distributed Representations for High-dimensional Sequences.
AI and Statistics, 2007, Puerto Rico. [pdf]
A longer version: Technical Report UTML TR 2006-003. [pdf]
2007 Cook, J. A., Sutskever, I., Mnih, A. and Hinton , G. E.
Visualizing similarity data with a mixture of maps.
AI and Statistics, 2007, Puerto Rico. [pdf]
2007 Taylor, G. W., Hinton, G. E. and Roweis, S.
Modeling human motion using binary latent variables.
Advances in Neural Information Processing Systems, 19
MIT Press, Cambridge, MA [pdf]
2006 Hinton, G. E. and Salakhutdinov, R. R
Reducing the dimensionality of data with neural networks.
Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
[ abstract ] [ full paper ] [ supporting online material (pdf) ] [ Matlab code ]
2006 Hinton, G. E., Osindero, S., Welling, M. and Teh, Y.
Unsupervised Discovery of Non-linear Structure using Contrastive Backpropagation.
Cognitive Science, 30:4, pp 725-731. [ps] [pdf]
2006 Hinton, G. E., Osindero, S. and Teh, Y.
A fast learning algorithm for deep belief nets.
Neural Computation 18, pp 1527-1554. [pdf]
2006 Hinton, G. E. and Nair, V.
Inferring motor programs from images of handwritten digits.
Advances in Neural Information Processing Systems, 18, MIT Press, Cambridge, MA [ps.gz] [pdf]
2006 Osindero, S., Welling, M. and Hinton, G. E.
Topographic Product Models Applied To Natural Scene Statistics
Neural Computation, 18, pp 381-344. [pdf]
2005 Hinton, G. E., Osindero, S. and Bao, K.
Learning Causally Linked Markov Random Fields.
In: Artificial Intelligence and Statistics, 2005, Barbados [ps.gz] [pdf]
2005 Hinton. G. E.
What kind of a graphical model is the brain?
International Joint Conference on Artificial Intelligence 2005, Edinburgh.
[ps.gz] [pdf]
2005 Mnih, A. and Hinton. G. E.
Learning Unreliable Constraints using Contrastive Divergence.
In IJCNN 2005, Montreal [ps.gz] [pdf]
2005 Memisevic, R. and Hinton, G. E.
Improving dimensionality reduction with spectral gradient descent.
Neural Networks, 18, pp 702-710. [online version]
2005 Carreira-Perpignan, M. A. and Hinton. G. E.
On Contrastive Divergence Learning.
In: Artificial Intelligence and Statistics, 2005, Barbados [pdf]
2005 Goldberger, J., Roweis, S., Salakhutdinov, R and Hinton, G. E.
Neighborhood Components Analysis
Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, MA [pdf]
2005 Memisevic, R. and Hinton, G. E.
Multiple Relational Embedding
Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, MA [ps.gz] [pdf]
2005 Welling, M,, Rosen-Zvi, M. and Hinton, G. E.
Exponential Family Harmoniums with an Application to Information Retrieval.
Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, MA [ps.gz] [pdf]
2004 Sallans, B and Hinton, G. E.
Reinforcement Learning with Factored States and Actions.
[Journal of Machine Learning Research, Vol 5 (Aug)] pp 1063--1088.
2004 Bishop, C. M. Svensen, M. and Hinton, G. E.
Distinguishing Text from Graphics in On-line Handwritten Ink.
In Kimura, F. and Fujisawa, H. (eds.), Proceedings Ninth International Workshop on Frontiers in Handwriting Recognition, IWFHR-9, Tokyo, Japan, pp. 142-147. [pdf]
2004 Hinton, G. E., Welling, M. and Mnih, A.
Wormholes Improve Contrastive Divergence.
Advances in Neural Information Processing Systems, 16, MIT Press, Cambridge, MA [ps.gz] [pdf]
2004 Welling, M., Zemel, R. and Hinton, G. E.
Probabilistic sequential independent components analysis.
IEEE Transactions on Neural Networks, Vol. 15, pp 838-849. [ps.gz] [pdf]
2003 Teh, Y. W, Welling, M., Osindero, S. and Hinton G. E.
Energy-Based Models for Sparse Overcomplete Representations.
Journal of Machine Learning Research, 4, pp 1235-1260. [ps.gz] [pdf]
2003 Hinton, G. E. and Roweis, S.
Stochastic Neighbor Embedding.
Advances in Neural Information Processing Systems, 15, MIT Press, Cambridge, MA
[ps.gz] [pdf]
2003 Welling, M., Zemel, R. S., and Hinton, G. E.
Efficient parametric projection pursuit density estimation.
In: UAI-2003: 19th Conference on Uncertainty in Artificial Intelligence.
[ps.gz]
2003 Welling, M., Zemel, R. and Hinton, G. E.
Self-Supervised Boosting.
Advances in Neural Information Processing Systems, 15, MIT Press, Cambridge, MA
[pdf] [ps.gz]
2003 Welling, M., Hinton, G. E. and Osindero, S.
Learning Sparse Topographic Representations with Products of Student-t Distributions.
Advances in Neural Information Processing Systems, 15, MIT Press, Cambridge, MA
[ps.gz] [pdf]
2003 Hinton, G. E.
The ups and downs of Hebb synapses.
Canadian Psychology, Vol 44, pp 10-13. [pdf]
2002 Hinton, G. E. (2002)
Training Products of Experts by Minimizing Contrastive Divergence.
Neural Computation, 14, pp 1771-1800. [pdf]
2002 Friston, K.J., Penny, W., Phillips, C., Kiebel, S., Hinton, G. E., and Ashburner, J.
Classical and Bayesian Inference in Neuroimaging: Theory.
NeuroImage, 16, pp 465-483. [pdf]
2002 Brown, A. D. and Hinton, G. E.
Relative Density Nets: A New Way to Combine Backpropagation with HMM's.
Advances in Neural Information Processing Systems, 14, MIT Press, Cambridge, MA
[pdf] [ps] [ps.gz]
2002 Paccanaro, A., and Hinton, G. E.
Learning Hierarchical Structures with Linear Relational Embedding.
Advances in Neural Information Processing Systems, 14, MIT Press, Cambridge, MA
[pdf] [ps] [ps.gz]
2002 Roweis, S., Saul, L. and Hinton, G. E.
Global Coordination of Local Linear Models
Advances in Neural Information Processing Systems, 14, MIT Press, Cambridge, MA
[.ps.gz] [pdf]
2002 Oore, S., Terzopoulos, D. and Hinton, G. E.
A Desktop Input Device and Interface for Interactive 3D Character Animation.
Graphics Interface. [pdf]
2002 Oore, S., Terzopoulos, D. and Hinton, G. E.
Local Physical Models for Interactive Character Animation.
Eurographics 2002, 21, Blackwell Publishers, Oxford. [pdf]
2002 Welling, M. and Hinton, G. E.
A New Learning Algorithm for Mean Field Boltzmann Machines.
ICANN, Madrid. In Dorronsoro J.R. (ed) Lecture notes in Computer Science Vol 2415, pp 351-357, Springer. [pdf]
2002 Guy Mayraz and Geoffrey Hinton (2002)
Recognizing Handwritten Digits using Hierarchical Products of Experts
IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, pp 189-197 [pdf]
2001 Hinton G. E., Welling, M., Teh, Y. W, and Osindero, S.
A New View of ICA.
Proceedings of ICA-2001, San Diego, CA. [ps.gz] [pdf]
2001 Hinton, G. E. and Teh, Y. W.
Discovering Multiple Constraints that are Frequently Approximately Satisfied.
Proceedings of Uncertainty in Artificial Intelligence (UAI-2001), pp 227-234. [ps.gz] [pdf]
2001 Andrew Brown, Geoffrey Hinton
Training Many Small Hidden Markov Models.
Proceedings of the Workshop on Innovation in Speech Processing. [ps] [ps.gz] [pdf]
2001 Andrew Brown, Geoffrey Hinton
Products of Hidden Markov Models.
T. Jaakkola and T. Richardson eds., Proceedings of Artificial Intelligence and Statistics 2001, Morgan Kaufmann, pp 3-11
[abstract] [ps] [ps.gz] [pdf]
2001 Yee-Whye Teh, Geoffrey Hinton
Rate-coded Restricted Boltzmann Machines for Face Recognition
Advances in Neural Information Processing Systems 13, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf]
2001 Brian Sallans, Geoffrey Hinton
Using Free Energies to Represent Q-values in a Multiagent Reinforcement learning Task
Advances in Neural Information Processing Systems 13, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf]
2001 Guy Mayraz, Geoffrey Hinton
Recognizing Hand-Written Digits Using Hierarchical Products of Experts
Advances in Neural Information Processing Systems 13, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf]
2000 Hinton, G.E.
Training Products of Experts by Minimizing Contrastive Divergence
Technical Report:  GCNU TR 2000-004
[abstract] [ps.gz] [pdf]
2000 Paccanaro, A. and Hinton, G.E
Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2000
[pdf] [ps] [ps.gz]
2000 Hinton, G.E.
Modelling High-Dimensional Data by Combining Simple Experts.
AAAI-2000: Seventeenth National Conference on Artificial Intelligence, Austin, Texas. [pdf]
2000 Paccanaro, A.,  and Hinton, G.E.
Learning Distributed Representations of Concepts from Relational Data Using Linear Relational Embedding
IEEE Transactions on Knowledge and Data Engineering, 13, pp 232-245. (Online preprint version is Technical Report: GCNU TR 2000-002)
[abstract] [ps.gz]] [pdf]
2000 Hinton, G.E. , and  Brown, A
Spiking Boltzmann machines. In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA
[abstract] [ps.gz] [pdf]
2000 Hinton, G.E. ,  Ghahramani, Z and Teh Y. W.
Learning to parse images.
In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA
[abstract] [ps.gz] [pdf]
2000 Ghahramani, Z. and Hinton, GE
Variational learning for switching state-space models.
[abstract] [ps.gz] [pdf]
Neural Computation, 12, pp 831-864,
2000 Ueda, N. Nakano, R., Ghahramani, Z and Hinton, G.E.
SMEM algorithm for mixture models. [abstract] [ps.gz] [pdf]
Neural Computation, 12, pp 2109-2128
2000 Paccanaro, A.,  and Hinton, G.E.
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space
ICML-2000, Proceedings of the Seventeenth International Conference on Machine Learning, Langley P. (Ed.), 711-718, Stanford University, Morgan Kaufmann Publishers, San Francisco.
[ps]] [ps.gz]] [pdf]
1999 Hinton, G.E.
Supervised learning in multilayer neural networks
in The MIT Encyclopedia of the Cognitive Sciences
Editors: Robert A. Wilson and Frank C. Keil
The MIT Press. [ps] [pdf]
1999 Hinton, G.E
Training products of experts by maximizing contrastive likelihood
Technical Report:  GCNU TR1999-001
[ See updated version above, GCNU TR 2000-004]
1999 Hinton, G.E.
Products of experts
Proceedings of the Ninth International Conference on Artificial Neural Networks [ICANN 99 Vol 1  pages 1-6]. [abstract] [ps] [pdf]
1999 Ghahramani, Z., Korenberg, A.T. and Hinton, G.E.
Scaling in a hierarchical unsupervised network.
[abstract] [ps.gz] [pdf]
Proceedings of the Ninth International Conference on Artificial Neural Networks [ICANN 99, Vol 1 pages 13-18]
1999 Hinton, G.E., and T. Sejnowski (eds)
Unsupervised Learning:  Foundations of Neural Computation
June 1999,  MIT Press
1999 Frey, B.J., Hinton, G.E.
Variational learning in nonlinear gaussian belief networks
[abstract] [ps] [pdf]
Neural computation  11:1, 193-214
1998 Neal, R.M. and Hinton, G.E.
A view of the EM algorithm that justifies incremental, sparse, and other variants
[abstract] [ps] [pdf]
in Learning in Graphical Model M.I. Jordan (editor)
1998 de Sa, V.R. and Hinton, G.E.
Cascaded redundancy reduction.
[abstract] [ps] [pdf]
Network: Computation in Neural Systems, 9, 73-84
1998 Tibshirani, R. and Hinton, G.E.
Coaching variables for regression and classification
[abstract] [ps] [pdf]
Statistics and Computing, 8, 25-33
1998 Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R.
A comparison of statistical learning methods on the GUSTO database.
Statistics in Medicine, 17, pp 2501-2508. [pdf]
1998 Ghahramani, Z. and Hinton, G.E
Hierarchical nonlinear factor analysis and topographic maps
[abstract] [ps.gz] [pdf]
Advances in Neural Information Processing Systems 10. MIT Press: Cambridge, MA.
1998 Grzeszczuk, R., Terzopoulos, D., and Hinton, G.~E.
NeuroAnimator: Fast Neural Network Emulation and Control of Physics-Based Models.
Proc. ACM SIGGRAPH-98, Computer Graphics Proceedings, Annual Conference Series, pp 9-20. [pdf]
1998 Fels, S. S. and Hinton, G. E.
Glove-TalkII: A neural network interface which maps gestures to parallel formant speech synthesizer controls.
[abstract] [ps] [pdf]
IEEE Transactions on Neural Networks, Vol 9 No 1, 205-212
1997 Hinton, G.E. , Sallans, B., and Ghahramani, Z.
A hierarchical community of experts
[abstract] [ps.gz] [pdf]
Learning in Graphical Model, 479-494,  Kluwer Academic Publishers
1997 Williams, C. K. I., Revow, M. and Hinton, G. E.
Instantiating deformable models with a neural net
[abstract] [ps]  [pdf]
Computer Vision and Image Understanding, Vol. 68, No. 1, Oct 1997, pp. 120-126
1997 Hinton, G.E.,   Dayan, P. and Revow, M.
Modeling the manifolds of images of handwritten digits.
IEEE Transactions on Neural Networks, 8 65-74
[abstract] [ps] [ps.gz] [pdf]
1997 Hinton, G.E. and Revow, M.
Using mixtures of factor analyzers for segmentation and pose estimation
[abstract] [ps] [ps.gz] [pdf]
1997 Hinton, G. E. and Ghahramani, Z.
Generative models for discovering sparse distributed representations
[abstract] [ps] [pdf]
Philosophical Transactions of the Royal Society of London, B, 352: 1177-1190
1997 Hinton, G.E. and Zemel, R.S.
Minimizing description length in an unsupervised neural network
[abstract] [ps] [pdf]
1997 Dayan, P. and Hinton, G. E.
Using EM for reinforcement learning
[abstract] [ps.gz] [pdf]
Neural Computation, 9, 271-278
1997 Oore, S, Hinton, G.E., and Dudek G.
A mobile robot that learns its place
[abstract] [ps] [pdf]
Neural Computation 9:3 683-699
1997 Frey, B.J. Hinton, G.E.
Efficient stochastic source coding and an application to a bayesian network source model
The computer Journal, 40, No. 2/3, 157-165. [abstract] [ps] [pdf]
1997 Bishop, C. M., Hinton, G.~E. and Strachan, I. D. G.
GTM through time.
Proceedings IEE Fifth International Conference on Artificial Neural Networks}. pp 111--116. IEE, London. [pdf]
1996 Frey, B. J., Hinton, G. E. and Dayan, P.
Does the wake-sleep algorithm learn good density estimators?
[abstract] [ps] [ps.gz] [pdf]
Advances in Neural Information Processing Systems 8. MIT Press, Cambridge, MA.
1996 Frey, B. J. and Hinton, G. E.
A simple algorithm that discovers efficient perceptual codes
[abstract] [ps] [pdf]
L. Harris and M. Jenkin (Eds) Computational and Biological Mechanisms of Visual Coding, Cambridge University press, New York.
1996 Dayan, P. and Hinton, G. E.
Varieties of Helmholtz machines.
Neural Networks, 9 1385-1403. [abstract] [pdf]
1996 Hinton, G. E. and Revow, M.
Using pairs of data-points to define splits for decision trees
[abstract] [ps] [pdf]
Advances in Neural Information Processing Systems 8. D.S. Touretzky, M.C. Mozer and M.E. Hasselmo. MIT Press.
1996 Ghahramani, Z. and Hinton, G. E.
The EM algorithm for mixtures of factor analyzers.
[abstract] [ps.gz] [pdf]
Technical Report CRG-TR-96-1, University of Toronto.
1996 Ghahramani, Z. and Hinton, G. E.
Parameter estimation for linear dynamical systems.
[abstract] [ps.gz] [pdf]
Technical Report CRG-TR-96-2, University of Toronto.
1996 Frey, B. J., and Hinton, G.~E.
Free energy coding.
Proceedings of the Data Compression Conference 1996, IEEE Computer Society Press, Los Alamitos, CA.
1996 Revow, M., Williams, C. K. I. and Hinton, G. E.
Using Generative Models for Handwritten Digit Recognition.
IEEE Transactions on Pattern Analysis and Machine Intelligence,18, 592-606.
[abstract] [ps] [pdf]
1995 Hinton, G. E., Revow, M. and Dayan P.
Recognizing handwritten digits using mixtures of linear models.
[abstract] [ps] [ps.gz] [pdf]
Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 1015-1022 MIT Press, Cambridge MA.
1995 Williams, C. K. I., Hinton, G. E. and Revow, M.
Using a neural net to instantiate a deformable model.
[abstract] [ps] [pdf]
Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 965-972 MIT Press, Cambridge MA.
1995 Xu, L., Jordan, M. I. and Hinton, G.~E.
An alternative model for mixtures of experts.
Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 633-640 MIT Press, Cambridge MA. [pdf]
1995 Fels, S. S. and Hinton, G.~E.
GloveTalkII: Mapping hand gestures to speech using neural networks.
Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 843-850 MIT Press, Cambridge MA. [pdf]
1995 Zemel, R. S. and Hinton, G. E.
Learning population codes by minimizing description length.
[abstract] [ps.gz] [pdf]
Neural Computation, 7, 549-564.
1995 Hinton, G. E., Dayan, P., Frey, B. J. and Neal, R.
The wake-sleep algorithm for unsupervised Neural Networks.
[abstract] [ps] [pdf]
Science, 268, 1158-1161.
1995 Dayan, P., Hinton, G. E., Neal, R., and Zemel, R. S.
The Helmholtz machine
[abstract] [ps.gz] [pdf]
Neural Computation, 7, 1022-1037.
1995 Hinton, G. E., Dayan, P., To, A. and Neal R. M.
The Helmholtz machine through time.
[abstract] [ps] [pdf]
F. Fogelman-Soulie and R. Gallinari (editors) ICANN-95, 483-490
1995 Hinton, G. E. and Frey, B. J.
Using neural networks to monitor for rare failures.
Proceedings of the 37th Mechanical Working and Steel Processing Conference, Hamilton, Ontario. [pdf]
1994 Hinton, G. E. and Zemel, R. S.
Autoencoders, minimum description length, and Helmholtz free energy.
[abstract] [ps] [pdf]
Advances in Neural Information Processing Systems 6. J. D. Cowan, G. Tesauro and J. Alspector (Eds.), Morgan Kaufmann: San Mateo, CA.
1994 Zemel, R.S. and Hinton, G. E.
Developing population codes by minimizing description length.
[abstract] [ps] [pdf]
Advances in Neural Information Processing Systems 6. J. D. Cowan, G. Tesauro and J. Alspector (Eds.), Morgan Kaufmann: San Mateo, CA.
1994 Xu, L. Jordan, M. I. and Hinton, G.~E.
A modified gating network for the mixtures of experts architectures.
Proc. WCNN-94, San Diego, CA. vol. 2, pp. 405-410. [pdf]
1993 Williams, C. K. I., Revow, M. and Hinton, G. E.
Hand-printed digit recognition using deformable models.
L. Harris and M. Jenkin (Eds)
Spatial Vision in Humans and Robots
Cambridge University press, New York.
1993 Revow, M., Williams, C.K.I, and Hinton, G.E.
Using mixtures of deformable models to capture variations in the shapes of hand-printed digits.
[abstract] [ps] [pdf]
Third International Workshop on Frontiers of Handwriting Recognition.
1993 Hinton, G. E. and van Camp, D.
Keeping neural networks simple by minimizing the description length of the weights
[abstract] [ps] [pdf]
Sixth ACM Conference on Computational Learning Theory, Santa Cruz, July 1993.
1993 Hinton, G. E., Plaut, D. C. and Shallice, T.
Simulating brain damage
Scientific American, October Issue [pdf]
1993 Becker, S. and Hinton, G. E.
Learning mixture models of spatial coherence
Neural Computation 5, 267-277. [abstract] [pdf]
1993 Nowlan. S. J. and Hinton, G. E.
A soft decision-directed LMS algorithm for blind equalization.
IEEE Transactions on Communications, 41, 275-279. [pdf]
1993 Dayan, P. and Hinton, G. E.
Feudal reinforcement learning.
[abstract] [ps.gz] [pdf]
Advances in Neural Information Processing Systems 5. S. J. Hanson, J. D. Cowan and C. L. Giles (Eds.), Morgan Kaufmann: San Mateo, CA.
1992 Fels, S. S. and Hinton, G. E.
Glove-Talk: A neural network interface between a data-glove and a speech synthesizer.
[abstract] [ps] [pdf]
IEEE Transactions on Neural Networks, 3, No 6
1992 Hinton, G. E., Williams, C. K. I., and Revow, M.
Combining two methods of recognizing hand-printed digits.
Artificial Neural Networks II: Proceedings of ICANN-92. I. Aleksander and J. Taylor (Eds.), Elsevier North-Holland. [pdf]
1992 Hinton, G. E., Williams, C. K. I., and Revow, M.
Adaptive elastic models for character recognition.
[abstract] [ps.gz] [pdf]
Advances in Neural Information Processing Systems 4. J. E. Moody, S. J. Hanson and R. P. Lippmann (Eds.), Morgan Kaufmann: San Mateo, CA.
1992 Nowlan. S. J. and Hinton, G. E.
Simplifying neural networks by soft weight sharing.
[pdf]
Neural Computation, 4, 173-193.
1992 Becker, S. and Hinton, G. E.
A self-organizing neural network that discovers surfaces in random-dot stereograms.
Nature, 355:6356, 161-163 [abstract] [pdf]
[Commentary by Graeme Mitchison and Richard Durbin in the News and Views section of Nature]
1992 Hinton, G.E.
How neural networks learn from experience.
Scientific American, September 1992. [pdf]
1991 Jacobs, R., Jordan, M. I., Nowlan. S. J. and Hinton, G. E.
Adaptive mixtures of local experts.
[abstract] [ps] [pdf]
Neural Computation, 3, 79-87.
1991 Nowlan, S. J. and Hinton, G. E.
Evaluation of adaptive mixtures of competing experts.
[abstract] [ps] [pdf]
Advances in Neural Information Processing Systems 3. R. P. Lippmann, J. E. Moody, and D. S. Touretzky (Eds.), Morgan Kaufmann: San Mateo, CA.
1991 Zemel, R.S. Hinton, G.E.
Discovering viewpoint-invariant relationships that characterize objects
Advances in Neural Information Processing Systems 3. R. P. Lippmann, J. E. Moody, and D. S. Touretzky (Eds.), Morgan Kaufmann: San Mateo, CA. [pdf]
1991 Hinton, G. E. and Shallice, T.
Lesioning an attractor network: Investigations of acquired dyslexia.
Psychological Review, 98, 74-95. [pdf]
1990 Williams, C. K. I. and Hinton, G. E.
Mean field networks that learn to discriminate temporally distorted strings.
Touretzky, D. S., Elman, J. L., Sejnowski, T. J. and Hinton, G. E. (Eds.) Connectionist Models: Proceedings of the 1990 Connectionist Summer School. Morgan Kauffman: San Mateo, CA. [abstract] [ps] [pdf]
1990 Zemel, R.S. Mozer, M.C., Hinton, G.E.
Traffic:  Recognizing objects using hierarchical reference frame transformations
Touretzky, D. S., (Ed.) Advances in Neural Information Processing Systems 2, Morgan Kaufmann: San Mateo, CA. [pdf]
1990 Galland, C. G. and Hinton, G. E.
Discovering higher-order features with mean field networks.
Touretzky, D. S., (Ed.) Advances in Neural Information Processing Systems 2, Morgan Kaufmann: San Mateo, CA. [pdf]
1990 Lang, K. J. and Hinton, G. E.
Dimensionality reduction and prior knowledge in E-set recognition.
Touretzky, D. S., (Ed.) Advances in Neural Information Processing Systems 2, Morgan Kaufmann: San Mateo, CA. [pdf]
1990 Galland, C. G. and Hinton, G. E.
Deterministic Boltzmann learning in networks with asymmetric connectivity.
Touretzky, D. S., Elman, J. L., Sejnowski, T. J. and Hinton, G. E. (Eds.) Connectionist Models: Proceedings of the 1990 Connectionist Summer School. Morgan Kauffman: San Mateo, CA.
1990 Hinton, G.E.
Preface to the special issue on connectionist symbol processing
Artificial Intelligence 46, 1-4. [ps] [pdf]
1990 Lang, K., Waibel, A. and Hinton, G. E.
A time-delay neural network architecture for isolated word recognition.
Neural Networks, 3, 23-43.[pdf]
1990 Hinton, G. E.
Mapping part-whole hierarchies into connectionist networks.
Artificial Intelligence, 46, 47-75. [pdf]
1990 Hinton, G.E., Becker, S.
An unsupervised learning procedure that discovers surfaces in random-dot stereograms
Proc. International Joint Conference on Neural Networks, Washington, DC, 1990 [pdf]
1990 Hinton, G. E. and Nowlan, S. J.
The bootstrap Widrow-Hoff rule as a cluster-formation algorithm.
Neural Computation, 2, 355-362. [pdf]
1989 Hinton, G. E.
Deterministic Boltzmann learning performs steepest descent in weight-space.
Neural Computation, 1, 143-150. [pdf]
1989 Waibel, A. Hanazawa, T. Hinton, G. Shikano, K. and Lang, K.
Phoneme recognition using time-delay neural networks.
IEEE Acoustics Speech and Signal Processing, 37, 328-339. [pdf]
1989 LeCun, Y., Galland, C. C., and Hinton, G. E.
GEMINI: Gradient Estimation by Matrix Inversion after Noise Injection.
Touretzky, D. S., (Ed.)  Neural Information Processing Systems 1, Morgan Kaufmann: San Mateo, CA. [pdf]
1989 Hinton, G.E.
Connectionist learning procedures.
Artificial Intelligence 40, 185-234. [pdf]
1988 Touretzky, D. S. and Hinton, G. E.
A distributed connectionist production system.
Cognitive Science, 12, 423-466. [pdf]
1988 Hinton, G. E. and Parsons, L. A.
Scene-based and viewer-centered representations for comparing shapes.
Cognition, 30, 1--35.[pdf]
1988 Hinton, G. E. and McClelland, J. L.
Learning representations by recirculation.
In D. Z. Anderson, editor, Neural Information Processing Systems, pages 358--366, American Institute of Physics: New York. [pdf]
1988 Hinton, G. E.
Representing part-whole hierarchies in connectionist networks.
Proceedings of the Tenth Annual Conference of the Cognitive Science Society. Montreal, Canada. [pdf]
1987 Fahlman, S. E. and Hinton, G. E.
Connectionist architectures for Artificial Intelligence.
IEEE Computer, 20, 100--109. [pdf]
1987 Hinton, G. E.
Learning translation invariant recognition in a massively parallel network.
In Goos, G. and Hartmanis, J., editors, PARLE: Parallel Architectures and Languages Europe, pages 1--13,
Lecture Notes in Computer Science, Springer-Verlag, Berlin. [pdf]
1987 Hinton, G.E.
The horizontal-vertical delusion
Perception, 16, 5, 677-680 [pdf]
1987 Hinton, G. E. and Plaut, D. C.
Using fast weights to deblur old memories.
Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA [pdf]
1987 Hinton, G. E. and Nowlan, S. J.
How learning can guide evolution.
Complex Systems, 1, 495--502. [ pdf ]
This paper was rejected by the Cognitive Science Society Conference (against the advice of the referees) because the chairman of the organizing committee thought it might mislead cognitive scientists.
[Commentary by John Maynard Smith in the News and Views section of Nature]
1987 Plaut, D.C. and Hinton, G.E.
Learning sets of filters using back-propagation
Computer Speech and Language, 2, 35-61 [ pdf ]
1987 Sejnowski, T.~J. and Hinton, G.~E.
Separating figure from ground using a Boltzmann machine.
In Arbib, M. and Hanson, A.~R., editors, Vision, Brain and Cooperative Computation,
MIT Press, Cambridge, MA. [pdf]
1986 Rumelhart, D. E., Hinton, G. E., and Williams, R. J.
Learning representations by back-propagating errors.
Nature, 323, 533--536.[pdf]
[Commentary from News and Views section of Nature]
1986 Kienker, P. K., Sejnowski, T. J., Hinton, G. E., and Schumacher, L. E.
Separating figure from ground with a parallel network.
Perception, 15, 197--216. [pdf]
1986 Sejnowski, T. J., Kienker, P. K., and Hinton, G. E.
Learning symmetry groups with hidden units: Beyond the perception. .Physica D, 22, 260--275. [pdf]
1986 Hinton, G. E.
Learning distributed representations of concepts. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, Mass. [ pdf ]
Reprinted in Morris, R. G. M. editor, Parallel Distributed Processing: Implications for Psychology and Neurobiology, Oxford University Press, Oxford, UK
1986 Plaut, D., Nowlan, S. and Hinton, G. E.
Experiments on learning by back-propagation
Technical Report CMU-CS-86-126.
Department of Computer Science, Carnegie-Mellon University. [ pdf ]
1986 McClelland, J. L., Rumelhart, D. E., and Hinton, G. E.
The appeal of for Parallel Distributed Processing
In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 3-44. [pdf]
1986 Rumelhart, D. E., Hinton, G. E., and McClelland, J. L.
A general framework for Parallel Distributed Processing
In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 45-76. [pdf]
1986 Hinton, G. E., McClelland, J. L., and Rumelhart, D. E.
Distributed representations.
In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 77-109. [pdf]
1986 Hinton, G. E. and Sejnowski, T. J.
Learning and relearning in Boltzmann machines.
In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 282-317. [pdf]
1986 Rumelhart, D. E., Hinton, G. E., and Williams, R. J.
Learning internal representations by error propagation.
In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 318-362. [pdf]
1986 Rumelhart, D. E., Smolensky, P., McClelland, J. L., and Hinton, G. E. 
Parallel distributed models of schemata and sequential thought processes.
In McClelland, J. L. and Rumelhart, D. E., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models, MIT Press, Cambridge, MA. pp 7-57. [pdf]
1986 Pearlmutter, B. A. and Hinton, G. E.
G-maximization: An unsupervised learning procedure for discovering regularities.
In Denker, J., editor, Neural Networks for Computing: American Institute of Physics Conference Proceedings, Vol 151, pp 333-338. [pdf]
1985 Ackley, D. H., Hinton, G. E., and Sejnowski, T. J.
A learning algorithm for Boltzmann machines.
Cognitive Science, 9, 147-169. [pdf]
1985 Touretzky, D. S. and Hinton, G. E.
Symbols among the neurons: Details of a connectionist inference architecture.
Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles. [pdf]
1985 Hinton, G.E. and Lang, K.J.
Shape recognition and illusory conjunctions
Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, pp 252-259 [pdf]
1985 Hinton, G.E.
Learning in parallel networks.
Byte, April issue. [pdf]
1985 Szeliski, R. and Hinton, G. E.
Solving random-dot stereograms using the heat equation.
Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, San Francisco.
1984 Hinton, G. E.
Parallel computations for controlling an arm.
The Journal of Motor Behavior, 16, 171-194. [pdf]
1984 Hinton, G.~E., Sejnowski, T. J., and Ackley, D. H.
Boltzmann Machines: Constraint satisfaction networks that learn.
Technical Report CMU-CS-84-119, Carnegie-Mellon University. [pdf]
1984 Hutchins, E. L. and Hinton, G. E.
Why the islands move.
Perception, 13, 629--632. [pdf]
1984 Hammond, N., Hinton, G.E., Barnard, P., Long, J. and Whitefield, A
Evaluating the interface of a document processor: A comparison of expert judgement and user observation.
Proceedings of the First IFIP Conference on Human-Comuter Interaction, North-Holland
1984 Hinton, G. E.
Some computational solutions to Bernstein's problems.
In Whiting, H., editor, Human Motor Actions: Bernstein Reassessed, North-Holland, New York. [pdf]
1983 Ballard, D. H., Hinton, G. E., and Sejnowski, T. J.
Parallel visual computation.
Nature, 306, 21--26.[pdf]
1983 Hinton, G. E. and Sejnowski, T. J.
Optimal perceptual inference.
Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Washington DC. [pdf]
1983 Fahlman, S.E., Hinton, G.E. and Sejnowski, T.J.
Massively parallel architectures for A.I.: Netl, Thistle, and Boltzmann machines.
Proceedings of the National Conference on Artificial Intelligence, Washington DC. [pdf]
1983 Hinton, G.E. and Sejnowski, T.J.
Analyzing cooperative computation.
Proceedings of the Fifth Annual Conference of the Cognitive Science Society, Rochester NY. [pdf]
1981 Hinton, G.E.
The role of spatial working memory in shape perception.
Proceedings of the Third Annual Conference of the Cognitive Science Society, Berkeley CA. [pdf]
1981 Hinton, G. and Anderson, J.
Parallel models of associative memory.
Lawrence Erlbaum Assoc., Hillsdale, NJ.
1981 Anderson, J. A. and Hinton, G. E.
Models of information processing in the brain.
In Hinton, G. E. and Anderson, J. A, editors, Parallel Models of Associative Memory, Erlbaum, Hillsdale, NJ. [pdf]
1981 Hinton, G. E.
Implementing semantic networks in parallel hardware.
In Hinton, G. E. and Anderson, J. A., editors, Parallel Models of Associative Memory, Erlbaum, Hillsdale, NJ. [pdf]
1981 Hinton, G.E.
A parallel computation that assigns canonical object-based frames of reference.
Proceedings of the Seventh International Joint Conference on Artificial Intelligence Vol 2, Vancouver BC, Canada [pdf]
1981 Hinton, G. E. and Parsons, L. A.
Frames of reference and mental imagery.
In Long, J. and Baddeley, A., editors, Attention and Performance IX, Erlbaum, Hillsdale, NJ. [pdf]
1981 Hinton, G.E.
Shape representation in parallel systems
Proceedings of the Seventh International Joint Conference on Artificial Intelligence Vol 2, Vancouver BC, Canada [pdf]
1979 Hinton, G. E.
Some demonstrations of the effects of structural descriptions in mental imagery.
Cognitive Science, 3, 231-250. [pdf]
1979 Hinton, G. E.
Imagery without Arrays.
Behavioral and Brain Sciences, 2, 555-556. [pdf]
1978 Sloman, A., Owen, D. Hinton., G., Birch, F. and O'Gorman, F.
Representation and control in vision.
Proceedings of the A.I.S.B. Summer Conference, Hamburg
1978 Hinton, G.E.
Respectively reconsidered
Pragmatics Microfiche, May issue
1978 Hinton, G. E.
Relaxation and its role in vision.
PhD Thesis, University of Edinburgh.
1976 Hinton, G.E.
Using relaxation to find a puppet
Proceeding of the A.I.S.B. Summer Conference, University of Edinburgh [pdf]

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