Geoffrey E. Hinton

Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is now an emeritus distinguished professor. From 2004 until 2013 he was the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research. Since 2013 he has been working half-time for Google in Mountain View and Toronto.

Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. He is an honorary foreign member of the American Academy of Arts and Sciences and the National Academy of Engineering, and a former president of the Cognitive Science Society. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and Engineering.

Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification.

Google Publications

Large scale distributed neural network training through online distillation

Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, George Dahl, Geoffrey Hinton

ICLR (2018)
Matrix capsules with EM routing

Geoffrey Hinton, Sara Sabour, Nicholas Frosst

ICLR (2018) (to appear)
Who said what: Modeling individual labelers improves classification

Melody Guan, Varun Gulshan, Andrew Dai, Geoffrey Hinton

AAAI (2018)
Distilling a Neural Network Into a Soft Decision Tree

Geoffrey Hinton, Nicholas Frosst

Comprehensibility and Explanation in AI and ML (CEX) @ AI*IA 2017 (2017)
Dynamic Routing between Capsules

Sara Sabour, Nicholas Frosst, Geoffrey Hinton

NIPS (2017) (to appear)
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean

ICLR (2017)
Regularizing Neural Networks by Penalizing Confident Output Distributions

Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, Geoffrey Hinton

ICLR Workshop (2017)
Who Said What: Modelling Individual Labels Improves Classification

Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey Hinton

CVPR Workshop (2017)
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton

NIPS (2016)
Using Fast Weights to Attend to the Recent Past

Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu

Google (2016)
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton

CoRR, vol. abs/1504.00941 (2015)
Distilling the Knowledge in a Neural Network

Geoffrey Hinton, Oriol Vinyals, Jeffrey Dean

NIPS Deep Learning and Representation Learning Workshop (2015)
Grammar as a Foreign Language

Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton

NIPS (2015)
Guest Editorial: Deep Learning

Marc'Aurelio Ranzato, Geoffrey E. Hinton, Yann LeCun

International Journal of Computer Vision, vol. 113 (2015), pp. 1-2
Autoregressive Product of Multi-frame Predictions Can Improve the Accuracy of Hybrid Models

Navdeep Jaitly, Vincent Vanhoucke, Geoffrey Hinton

Proceedings of Interspeech 2014
On Rectified Linear Units For Speech Processing

M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G.E. Hinton

38th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver (2013)

Previous Publications

Application of Deep Belief Networks for Natural Language Understanding

Ruhi Sarikaya, Geoffrey E. Hinton, Anoop Deoras

IEEE/ACM Trans. Audio, Speech & Language Processing, vol. 22 (2014), pp. 778-784
Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov

Journal of Machine Learning Research, vol. 15 (2014), pp. 1929-1958
Where Do Features Come From?

Geoffrey Hinton

Cognitive Science, vol. 38 (2014), pp. 1078-1101
Discovering Multiple Constraints that are Frequently Approximately Satisfied

Geoffrey E. Hinton, Yee Whye Teh

CoRR, vol. abs/1301.2278 (2013)
Improving deep neural networks for LVCSR using rectified linear units and dropout

George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton

ICASSP (2013), pp. 8609-8613
Modeling Documents with Deep Boltzmann Machines

Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton

CoRR, vol. abs/1309.6865 (2013)
Modeling Documents with Deep Boltzmann Machines

Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton

UAI (2013)
Modeling Natural Images Using Gated MRFs

Marc'Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton

IEEE Trans. Pattern Anal. Mach. Intell., vol. 35 (2013), pp. 2206-2222
New types of deep neural network learning for speech recognition and related applications: an overview

Li Deng, Geoffrey E. Hinton, Brian Kingsbury

ICASSP (2013), pp. 8599-8603
On the importance of initialization and momentum in deep learning

Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton

ICML (3) (2013), pp. 1139-1147
Speech Recognition with Deep Recurrent Neural Networks

Alex Graves, Abdel-rahman Mohamed, Geoffrey E. Hinton

CoRR, vol. abs/1303.5778 (2013)
Speech recognition with deep recurrent neural networks

Alex Graves, Abdel-rahman Mohamed, Geoffrey E. Hinton

ICASSP (2013), pp. 6645-6649
Tensor Analyzers

Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton

ICML (3) (2013), pp. 163-171
Using an autoencoder with deformable templates to discover features for automated speech recognition

Navdeep Jaitly, Geoffrey E. Hinton

INTERSPEECH (2013), pp. 1737-1740
A Better Way to Pretrain Deep Boltzmann Machines

Ruslan Salakhutdinov, Geoffrey E. Hinton

NIPS (2012), pp. 2456-2464
A Practical Guide to Training Restricted Boltzmann Machines

Geoffrey E. Hinton

Neural Networks: Tricks of the Trade (2nd ed.) (2012), pp. 599-619
Acoustic Modeling Using Deep Belief Networks

Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton

IEEE Trans. Audio, Speech & Language Processing, vol. 20 (2012), pp. 14-22
An Efficient Learning Procedure for Deep Boltzmann Machines

Ruslan Salakhutdinov, Geoffrey E. Hinton

Neural Computation, vol. 24 (2012), pp. 1967-2006
Conditional Restricted Boltzmann Machines for Structured Output Prediction

Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton

CoRR, vol. abs/1202.3748 (2012)
Deep Lambertian Networks

Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton

ICML (2012)
Deep Mixtures of Factor Analysers

Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton

CoRR, vol. abs/1206.4635 (2012)
Deep Mixtures of Factor Analysers

Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton

ICML (2012)
Deep Neural Networks for Acoustic Modeling in Speech Recognition

Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury

Signal Processing Magazine (2012)
Efficient Parametric Projection Pursuit Density Estimation

Max Welling, Richard S. Zemel, Geoffrey E. Hinton

CoRR, vol. abs/1212.2513 (2012)
ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

NIPS (2012), pp. 1106-1114
ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

NIPS (2012)
Improving neural networks by preventing co-adaptation of feature detectors

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov

CoRR, vol. abs/1207.0580 (2012)
Introduction to the Special Section on Deep Learning for Speech and Language Processing

Dong Yu, Geoffrey E. Hinton, Nelson Morgan, Jen-Tzung Chien, Shigeki Sagayama

IEEE Trans. Audio, Speech & Language Processing, vol. 20 (2012), pp. 4-6
Learning to Label Aerial Images from Noisy Data

Volodymyr Mnih, Geoffrey E. Hinton

ICML (2012)
Products of Hidden Markov Models: It Takes N>1 to Tango

Graham W. Taylor, Geoffrey E. Hinton

CoRR, vol. abs/1205.2614 (2012)
Robust Boltzmann Machines for recognition and denoising

Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton

CVPR (2012), pp. 2264-2271
Understanding how Deep Belief Networks perform acoustic modelling

Abdel-rahman Mohamed, Geoffrey E. Hinton, Gerald Penn

ICASSP (2012), pp. 4273-4276
Visualizing non-metric similarities in multiple maps

Laurens van der Maaten, Geoffrey E. Hinton

Machine Learning, vol. 87 (2012), pp. 33-55
A better way to learn features: technical perspective

Geoffrey E. Hinton

Commun. ACM, vol. 54 (2011), pp. 94
Conditional Restricted Boltzmann Machines for Structured Output Prediction

Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton

UAI (2011), pp. 514-522
Deep Belief Networks using discriminative features for phone recognition

Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, Michael A. Picheny

ICASSP (2011), pp. 5060-5063
Deep belief nets for natural language call-routing

Ruhi Sarikaya, Geoffrey E. Hinton, Bhuvana Ramabhadran

ICASSP (2011), pp. 5680-5683
Discovering Binary Codes for Documents by Learning Deep Generative Models

Geoffrey E. Hinton, Ruslan Salakhutdinov

topiCS, vol. 3 (2011), pp. 74-91
Generating Text with Recurrent Neural Networks

Ilya Sutskever, James Martens, Geoffrey E. Hinton

ICML (2011), pp. 1017-1024
Learning a better representation of speech soundwaves using restricted boltzmann machines

Navdeep Jaitly, Geoffrey E. Hinton

ICASSP (2011), pp. 5884-5887
Modeling the joint density of two images under a variety of transformations

Joshua M. Susskind, Geoffrey E. Hinton, Roland Memisevic, Marc Pollefeys

CVPR (2011), pp. 2793-2800
On deep generative models with applications to recognition

Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton

CVPR (2011), pp. 2857-2864
Transforming Auto-Encoders

Geoffrey E. Hinton, Alex Krizhevsky, Sida D. Wang

ICANN (1) (2011), pp. 44-51
Two Distributed-State Models For Generating High-Dimensional Time Series

Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis

Journal of Machine Learning Research, vol. 12 (2011), pp. 1025-1068
Using very deep autoencoders for content-based image retrieval

Alex Krizhevsky, Geoffrey E. Hinton

ESANN (2011)
Binary coding of speech spectrograms using a deep auto-encoder

Li Deng, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, Geoffrey E. Hinton

INTERSPEECH (2010), pp. 1692-1695
Boltzmann Machines

Geoffrey E. Hinton

Encyclopedia of Machine Learning (2010), pp. 132-136
Comparing Classification Methods for Longitudinal fMRI Studies

Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Strother

Neural Computation, vol. 22 (2010), pp. 2729-2762
Deep Belief Nets

Geoffrey E. Hinton

Encyclopedia of Machine Learning (2010), pp. 267-269
Dynamical binary latent variable models for 3D human pose tracking

Graham W. Taylor, Leonid Sigal, David J. Fleet, Geoffrey E. Hinton

CVPR (2010), pp. 631-638
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton

AISTATS (2010), pp. 621-628
Gated Softmax Classification

Roland Memisevic, Christopher Zach, Geoffrey E. Hinton, Marc Pollefeys

NIPS (2010), pp. 1603-1611
Generating more realistic images using gated MRF's

Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton

NIPS (2010), pp. 2002-2010
Learning to Detect Roads in High-Resolution Aerial Images

Volodymyr Mnih, Geoffrey E. Hinton

ECCV (6) (2010), pp. 210-223
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines

Roland Memisevic, Geoffrey E. Hinton

Neural Computation, vol. 22 (2010), pp. 1473-1492
Learning to combine foveal glimpses with a third-order Boltzmann machine

Hugo Larochelle, Geoffrey E. Hinton

NIPS (2010), pp. 1243-1251
Modeling pixel means and covariances using factorized third-order boltzmann machines

Marc'Aurelio Ranzato, Geoffrey E. Hinton

CVPR (2010), pp. 2551-2558
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton

NIPS (2010), pp. 469-477
Phone recognition using Restricted Boltzmann Machines

Abdel-rahman Mohamed, Geoffrey E. Hinton

ICASSP (2010), pp. 4354-4357
Rectified Linear Units Improve Restricted Boltzmann Machines

Vinod Nair, Geoffrey E. Hinton

ICML (2010), pp. 807-814
Temporal-Kernel Recurrent Neural Networks

Ilya Sutskever, Geoffrey E. Hinton

Neural Networks, vol. 23 (2010), pp. 239-243
3D Object Recognition with Deep Belief Nets

Vinod Nair, Geoffrey E. Hinton

NIPS (2009), pp. 1339-1347
Deep Boltzmann Machines

Ruslan Salakhutdinov, Geoffrey E. Hinton

AISTATS (2009), pp. 448-455
Deep belief networks

Geoffrey E. Hinton

Scholarpedia, vol. 4 (2009), pp. 5947
Factored conditional restricted Boltzmann Machines for modeling motion style

Graham W. Taylor, Geoffrey E. Hinton

ICML (2009), pp. 1025-1032
Improving a statistical language model through non-linear prediction

Andriy Mnih, Zhang Yuecheng, Geoffrey E. Hinton

Neurocomputing, vol. 72 (2009), pp. 1414-1418
Learning Generative Texture Models with extended Fields-of-Experts

Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton

BMVC (2009), pp. 1-11
Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine

Matthew D. Zeiler, Graham W. Taylor, Nikolaus F. Troje, Geoffrey E. Hinton

ESANN (2009)
Products of Hidden Markov Models: It Takes N>1 to Tango

Graham W. Taylor, Geoffrey E. Hinton

UAI (2009), pp. 522-529
Replicated Softmax: an Undirected Topic Model

Ruslan Salakhutdinov, Geoffrey E. Hinton

NIPS (2009), pp. 1607-1614
Semantic hashing

Ruslan Salakhutdinov, Geoffrey E. Hinton

Int. J. Approx. Reasoning, vol. 50 (2009), pp. 969-978
Using fast weights to improve persistent contrastive divergence

Tijmen Tieleman, Geoffrey E. Hinton

ICML (2009), pp. 1033-1040
Workshop summary: Workshop on learning feature hierarchies

Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio

ICML (2009), pp. 5
Zero-shot Learning with Semantic Output Codes

Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell

NIPS (2009), pp. 1410-1418
A Scalable Hierarchical Distributed Language Model

Andriy Mnih, Geoffrey E. Hinton

NIPS (2008), pp. 1081-1088
Analysis-by-Synthesis by Learning to Invert Generative Black Boxes

Vinod Nair, Joshua M. Susskind, Geoffrey E. Hinton

ICANN (1) (2008), pp. 971-981
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators

Ilya Sutskever, Geoffrey E. Hinton

Neural Computation, vol. 20 (2008), pp. 2629-2636
Generative versus discriminative training of RBMs for classification of fMRI images

Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother

NIPS (2008), pp. 1409-1416
Implicit Mixtures of Restricted Boltzmann Machines

Vinod Nair, Geoffrey E. Hinton

NIPS (2008), pp. 1145-1152
Improving a statistical language model by modulating the effects of context words

Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton

ESANN (2008), pp. 493-498
The Recurrent Temporal Restricted Boltzmann Machine

Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor

NIPS (2008), pp. 1601-1608
Using matrices to model symbolic relationship

Ilya Sutskever, Geoffrey E. Hinton

NIPS (2008), pp. 1593-1600
Boltzmann machine

Geoffrey E. Hinton

Scholarpedia, vol. 2 (2007), pp. 1668
Learning Multilevel Distributed Representations for High-Dimensional Sequences

Ilya Sutskever, Geoffrey E. Hinton

AISTATS (2007), pp. 548-555
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure

Ruslan Salakhutdinov, Geoffrey E. Hinton

AISTATS (2007), pp. 412-419
Modeling image patches with a directed hierarchy of Markov random fields

Simon Osindero, Geoffrey E. Hinton

NIPS (2007), pp. 1121-1128
Restricted Boltzmann machines for collaborative filtering

Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton

ICML (2007), pp. 791-798
Three new graphical models for statistical language modelling

Andriy Mnih, Geoffrey E. Hinton

ICML (2007), pp. 641-648
Unsupervised Learning of Image Transformations

Roland Memisevic, Geoffrey E. Hinton

CVPR (2007)
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes

Ruslan Salakhutdinov, Geoffrey E. Hinton

NIPS (2007), pp. 1249-1256
Visualizing Similarity Data with a Mixture of Maps

James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton

AISTATS (2007), pp. 67-74
A Fast Learning Algorithm for Deep Belief Nets

Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh

Neural Computation, vol. 18 (2006), pp. 1527-1554
Modeling Human Motion Using Binary Latent Variables

Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis

NIPS (2006), pp. 1345-1352
Topographic Product Models Applied to Natural Scene Statistics

Simon Osindero, Max Welling, Geoffrey E. Hinton

Neural Computation, vol. 18 (2006), pp. 381-414
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

Geoffrey E. Hinton, Simon Osindero, Max Welling, Yee Whye Teh

Cognitive Science, vol. 30 (2006), pp. 725-731
Improving dimensionality reduction with spectral gradient descent

Roland Memisevic, Geoffrey E. Hinton

Neural Networks, vol. 18 (2005), pp. 702-710
Inferring Motor Programs from Images of Handwritten Digits

Geoffrey E. Hinton, Vinod Nair

NIPS (2005), pp. 515-522
Learning Causally Linked Markov Random Fields

Geoffrey E. Hinton, Simon Osindero, Kejie Bao

AISTATS (2005)
On Contrastive Divergence Learning

Miguel Á. Carreira-Perpiñán, Geoffrey Hinton

AISTATS (2005)
What kind of graphical model is the brain?

Geoffrey E. Hinton

IJCAI (2005), pp. 1765-
Exponential Family Harmoniums with an Application to Information Retrieval

Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton

NIPS (2004), pp. 1481-1488
Multiple Relational Embedding

Roland Memisevic, Geoffrey E. Hinton

NIPS (2004), pp. 913-920
Neighbourhood Components Analysis

Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov

NIPS (2004), pp. 513-520
Probabilistic sequential independent components analysis

Max Welling, Richard S. Zemel, Geoffrey E. Hinton

IEEE Trans. Neural Networks, vol. 15 (2004), pp. 838-849
Reinforcement Learning with Factored States and Actions

Brian Sallans, Geoffrey E. Hinton

Journal of Machine Learning Research, vol. 5 (2004), pp. 1063-1088
Efficient Parametric Projection Pursuit Density Estimation

Max Welling, Richard S. Zemel, Geoffrey E. Hinton

UAI (2003), pp. 575-582
Energy-Based Models for Sparse Overcomplete Representations

Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton

Journal of Machine Learning Research, vol. 4 (2003), pp. 1235-1260
Wormholes Improve Contrastive Divergence

Geoffrey E. Hinton, Max Welling, Andriy Mnih

NIPS (2003), pp. 417-424
A Desktop Input Device and Interface for Interactive 3D Character Animation

Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton

Graphics Interface (2002), pp. 133-140
A New Learning Algorithm for Mean Field Boltzmann Machines

Max Welling, Geoffrey E. Hinton

ICANN (2002), pp. 351-357
In Memory of Ray Reiter (1939-2002)

Fiora Pirri, Geoffrey E. Hinton, Hector J. Levesque

AI Magazine, vol. 23 (2002), pp. 93
Learning Sparse Topographic Representations with Products of Student-t Distributions

Max Welling, Geoffrey E. Hinton, Simon Osindero

NIPS (2002), pp. 1359-1366
Local Physical Models for Interactive Character Animation

Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton

Comput. Graph. Forum, vol. 21 (2002), pp. 337-346
Recognizing Handwritten Digits Using Hierarchical Products of Experts

Guy Mayraz, Geoffrey E. Hinton

IEEE Trans. Pattern Anal. Mach. Intell., vol. 24 (2002), pp. 189-197
Self Supervised Boosting

Max Welling, Richard S. Zemel, Geoffrey E. Hinton

NIPS (2002), pp. 665-672
Stochastic Neighbor Embedding

Geoffrey E. Hinton, Sam T. Roweis

NIPS (2002), pp. 833-840
Training Products of Experts by Minimizing Contrastive Divergence

Geoffrey E. Hinton

Neural Computation, vol. 14 (2002), pp. 1771-1800
Discovering Multiple Constraints that are Frequently Approximately Satisfied

Geoffrey E. Hinton, Yee Whye Teh

UAI (2001), pp. 227-234
Global Coordination of Local Linear Models

Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton

NIPS (2001), pp. 889-896
Learning Distributed Representations of Concepts Using Linear Relational Embedding

Alberto Paccanaro, Geoffrey E. Hinton

IEEE Trans. Knowl. Data Eng., vol. 13 (2001), pp. 232-244
Learning Hierarchical Structures with Linear Relational Embedding

Alberto Paccanaro, Geoffrey E. Hinton

NIPS (2001), pp. 857-864
Products of Hidden Markov Models

Andrew D. Brown, Geoffrey E. Hinton

AISTATS (2001)
Relative Density Nets: A New Way to Combine Backpropagation with HMM's

Andrew D. Brown, Geoffrey E. Hinton

NIPS (2001), pp. 1149-1156
Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions

Alberto Paccanaro, Geoffrey E. Hinton

IJCNN (2) (2000), pp. 259-264
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space

Alberto Paccanaro, Geoffrey E. Hinton

ICML (2000), pp. 711-718
Modeling High-Dimensional Data by Combining Simple Experts

Geoffrey E. Hinton

AAAI/IAAI (2000), pp. 1159-1164
Rate-coded Restricted Boltzmann Machines for Face Recognition

Yee Whye Teh, Geoffrey E. Hinton

NIPS (2000), pp. 908-914
Recognizing Hand-written Digits Using Hierarchical Products of Experts

Guy Mayraz, Geoffrey E. Hinton

NIPS (2000), pp. 953-959
SMEM Algorithm for Mixture Models

Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton

Neural Computation, vol. 12 (2000), pp. 2109-2128
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates

Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton

VLSI Signal Processing, vol. 26 (2000), pp. 133-140
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task

Brian Sallans, Geoffrey E. Hinton

NIPS (2000), pp. 1075-1081
Variational Learning for Switching State-Space Models

Zoubin Ghahramani, Geoffrey E. Hinton

Neural Computation, vol. 12 (2000), pp. 831-864
Learning to Parse Images

Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh

NIPS (1999), pp. 463-469
Spiking Boltzmann Machines

Geoffrey E. Hinton, Andrew D. Brown

NIPS (1999), pp. 122-128
Variational Learning in Nonlinear Gaussian Belief Networks

Brendan J. Frey, Geoffrey E. Hinton

Neural Computation, vol. 11 (1999), pp. 193-213
Coaching variables for regression and classification

Robert Tibshirani, Geoffrey Hinton

Statistics and Computing, vol. 8 (1998), pp. 25-33
Fast Neural Network Emulation of Dynamical Systems for Computer Animation

Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton

NIPS (1998), pp. 882-888
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls

S. Sidney Fels, Geoffrey E. Hinton

IEEE Trans. Neural Networks, vol. 9 (1998), pp. 205-212
NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models

Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton

SIGGRAPH (1998), pp. 9-20
SMEM Algorithm for Mixture Models

Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton

NIPS (1998), pp. 599-605
A Mobile Robot that Learns its Place

Sageev Oore, Geoffrey E. Hinton, Gregory Dudek

Neural Computation, vol. 9 (1997), pp. 683-699
Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model

Brendan J. Frey, Geoffrey E. Hinton

Comput. J., vol. 40 (1997), pp. 157-165
Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls

S. Sidney Fels, Geoffrey E. Hinton

IEEE Trans. Neural Networks, vol. 8 (1997), pp. 977-984
Hierarchical Non-linear Factor Analysis and Topographic Maps

Zoubin Ghahramani, Geoffrey E. Hinton

NIPS (1997), pp. 486-492
Instantiating Deformable Models with a Neural Net

Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton

Computer Vision and Image Understanding, vol. 68 (1997), pp. 120-126
Modeling the manifolds of images of handwritten digits

Geoffrey E. Hinton, Peter Dayan, Michael Revow

IEEE Trans. Neural Networks, vol. 8 (1997), pp. 65-74
Using Expectation-Maximization for Reinforcement Learning

Peter Dayan, Geoffrey E. Hinton

Neural Computation, vol. 9 (1997), pp. 271-278
Free Energy Coding

Brendan J. Frey, Geoffrey E. Hinton

Data Compression Conference (1996), pp. 73-81
Varieties of Helmholtz Machine

Peter Dayan, Geoffrey E. Hinton

Neural Networks, vol. 9 (1996), pp. 1385-1403
Does the Wake-sleep Algorithm Produce Good Density Estimators?

Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan

NIPS (1995), pp. 661-667
GloveTalkII: An Adaptive Gesture-to-Formant Interface

Sidney Fels, Geoffrey E. Hinton

CHI (1995), pp. 456-463
The Helmholtz machine

Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel

Neural Computation, vol. 7 (1995), pp. 889-904
Using Pairs of Data-Points to Define Splits for Decision Trees

Geoffrey E. Hinton, Michael Revow

NIPS (1995), pp. 507-513
An Alternative Model for Mixtures of Experts

Lei Xu 0001, Michael I. Jordan, Geoffrey E. Hinton

NIPS (1994), pp. 633-640
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks

Sidney Fels, Geoffrey E. Hinton

NIPS (1994), pp. 843-850
Recognizing Handwritten Digits Using Mixtures of Linear Models

Geoffrey E. Hinton, Michael Revow, Peter Dayan

NIPS (1994), pp. 1015-1022
A soft decision-directed LMS algorithm for blind equalization

Steven J. Nowlan, Geoffrey E. Hinton

IEEE Trans. Communications, vol. 41 (1993), pp. 275-279
Autoencoders, Minimum Description Length and Helmholtz Free Energy

Geoffrey E. Hinton, Richard S. Zemel

NIPS (1993), pp. 3-10
Developing Population Codes by Minimizing Description Length

Richard S. Zemel, Geoffrey E. Hinton

NIPS (1993), pp. 11-18
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer

S. Sidney Fels, Geoffrey E. Hinton

IEEE Trans. Neural Networks, vol. 4 (1993), pp. 2-8
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights

Geoffrey E. Hinton, Drew van Camp

COLT (1993), pp. 5-13
Learning Mixture Models of Spatial Coherence

Suzanna Becker, Geoffrey E. Hinton

Neural Computation, vol. 5 (1993), pp. 267-277
Feudal Reinforcement Learning

Peter Dayan, Geoffrey E. Hinton

NIPS (1992), pp. 271-278
Simplifying Neural Networks by Soft Weight-Sharing

Steven J. Nowlan, Geoffrey E. Hinton

Neural Computation, vol. 4 (1992), pp. 473-493
Adaptive Mixtures of Local Experts

Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton

Neural Computation, vol. 3 (1991), pp. 79-87
Adaptive Soft Weight Tying using Gaussian Mixtures

Steven J. Nowlan, Geoffrey E. Hinton

NIPS (1991), pp. 993-1000
Learning to Make Coherent Predictions in Domains with Discontinuities

Suzanna Becker, Geoffrey E. Hinton

NIPS (1991), pp. 372-379
A time-delay neural network architecture for isolated word recognition

Kevin J. Lang, Alex Waibel, Geoffrey E. Hinton

Neural Networks, vol. 3 (1990), pp. 23-43
Building adaptive interfaces with neural networks: The glove-talk pilot study

Sidney Fels, Geoffrey E. Hinton

INTERACT (1990), pp. 683-688
Connectionist Symbol Processing - Preface

Geoffrey E. Hinton

Artif. Intell., vol. 46 (1990), pp. 1-4
Discovering Viewpoint-Invariant Relationships That Characterize Objects

Richard S. Zemel, Geoffrey E. Hinton

NIPS (1990), pp. 299-305
Evaluation of Adaptive Mixtures of Competing Experts

Steven J. Nowlan, Geoffrey E. Hinton

NIPS (1990), pp. 774-780
Mapping Part-Whole Hierarchies into Connectionist Networks

Geoffrey E. Hinton

Artif. Intell., vol. 46 (1990), pp. 47-75
The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm

Geoffrey E. Hinton, Steven J. Nowlan

Neural Computation, vol. 2 (1990), pp. 355-362
Connectionist Learning Procedures

Geoffrey E. Hinton

Artif. Intell., vol. 40 (1989), pp. 185-234
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space

Geoffrey E. Hinton

Neural Computation, vol. 1 (1989), pp. 143-150
Dimensionality Reduction and Prior Knowledge in E-Set Recognition

Kevin J. Lang, Geoffrey E. Hinton

NIPS (1989), pp. 178-185
Discovering High Order Features with Mean Field Modules

Conrad C. Galland, Geoffrey E. Hinton

NIPS (1989), pp. 509-515
Phoneme recognition using time-delay neural networks

Alexander H. Waibel, Toshiyuki Hanazawa, Geoffrey E. Hinton, Kiyohiro Shikano, Kevin J. Lang

IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 37 (1989), pp. 328-339
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations

Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton

NIPS (1989), pp. 266-273
A Distributed Connectionist Production System

David S. Touretzky, Geoffrey E. Hinton

Cognitive Science, vol. 12 (1988), pp. 423-466
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection

Yann LeCun, Conrad C. Galland, Geoffrey E. Hinton

NIPS (1988), pp. 141-148
Connectionist Architectures for Artificial Intelligence

Scott E. Fahlman, Geoffrey E. Hinton

IEEE Computer, vol. 20 (1987), pp. 100-109
Learning Representations by Recirculation

Geoffrey E. Hinton, James L. McClelland

NIPS (1987), pp. 358-366
Learning Translation Invariant Recognition in Massively Parallel Networks

Geoffrey E. Hinton

PARLE (1) (1987), pp. 1-13
Learning in Massively Parallel Nets (Panel)

Drew V. McDermott, Geoffrey E. Hinton

AAAI (1986), pp. 1149
A Learning Algorithm for Boltzmann Machines

David H. Ackley, Geoffrey E. Hinton, Terrence J. Sejnowski

Cognitive Science, vol. 9 (1985), pp. 147-169
Shape Recognition and Illusory Conjunctions

Geoffrey E. Hinton, Kevin J. Lang

IJCAI (1985), pp. 252-259
Symbols Among the Neurons: Details of a Connectionist Inference Architecture

David S. Touretzky, Geoffrey E. Hinton

IJCAI (1985), pp. 238-243
Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines

Scott E. Fahlman, Geoffrey E. Hinton, Terrence J. Sejnowski

AAAI (1983), pp. 109-113
A Parallel Computation that Assigns Canonical Object-Based Frames of Reference

Geoffrey E. Hinton

IJCAI (1981), pp. 683-685
Shape Representation in Parallel Systems

Geoffrey E. Hinton

IJCAI (1981), pp. 1088-1096
Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery

Geoffrey E. Hinton

Cognitive Science, vol. 3 (1979), pp. 231-250
Representation and Control in Vision

Aaron Sloman, David Owen, Geoffrey E. Hinton, Frank Birch, Frank O'Gorman

AISB/GI (ECAI) (1978), pp. 309-314
Using Relaxation to find a Puppet

Geoffrey E. Hinton

AISB (ECAI) (1976), pp. 148-157

Geoffrey E. Hinton

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