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  2013 (11)
Discovering Multiple Constraints that are Frequently Approximately Satisfied. Hinton, G. E.; and Teh, Y. W. CoRR, abs/1301.2278. 2013.
Discovering Multiple Constraints that are Frequently Approximately Satisfied [link] link   Discovering Multiple Constraints that are Frequently Approximately Satisfied [link] psgz   Discovering Multiple Constraints that are Frequently Approximately Satisfied [pdf] pdf   link   bibtex   3 downloads  
Modeling Documents with Deep Boltzmann Machines. Srivastava, N.; Salakhutdinov, R.; and Hinton, G. E. CoRR, abs/1309.6865. 2013.
Modeling Documents with Deep Boltzmann Machines [link] link   Modeling Documents with Deep Boltzmann Machines [pdf] pdf   link   bibtex   9 downloads  
Speech Recognition with Deep Recurrent Neural Networks. Graves, A.; Mohamed, A.; and Hinton, G. E. CoRR, abs/1303.5778. 2013.
Speech Recognition with Deep Recurrent Neural Networks [link] link   Speech Recognition with Deep Recurrent Neural Networks [pdf] pdf   link   bibtex   27 downloads  
On rectified linear units for speech processing. Zeiler, M. D.; Ranzato, M.; Monga, R.; Mao, M. Z.; Yang, K.; Le, Q. V.; Nguyen, P.; Senior, A. W.; Vanhoucke, V.; Dean, J.; and Hinton, G. E. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 3517-3521, 2013.
On rectified linear units for speech processing [link] link   On rectified linear units for speech processing [pdf] pdf   link   bibtex  
Speech recognition with deep recurrent neural networks. Graves, A.; Mohamed, A.; and Hinton, G. E. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 6645-6649, 2013.
Speech recognition with deep recurrent neural networks [link] link   Speech recognition with deep recurrent neural networks [pdf] pdf   link   bibtex   27 downloads  
New types of deep neural network learning for speech recognition and related applications: an overview. Deng, L.; Hinton, G. E.; and Kingsbury, B. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 8599-8603, 2013.
New types of deep neural network learning for speech recognition and related applications: an overview [link] link   New types of deep neural network learning for speech recognition and related applications: an overview [pdf] pdf   link   bibtex   5 downloads  
Improving deep neural networks for LVCSR using rectified linear units and dropout. Dahl, G. E.; Sainath, T. N.; and Hinton, G. E. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 8609-8613, 2013.
Improving deep neural networks for LVCSR using rectified linear units and dropout [link] link   Improving deep neural networks for LVCSR using rectified linear units and dropout [pdf] pdf   link   bibtex   4 downloads  
Tensor Analyzers. Tang, Y.; Salakhutdinov, R.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 163-171, 2013.
Tensor Analyzers [link] link   Tensor Analyzers [pdf] pdf   link   bibtex   4 downloads  
On the importance of initialization and momentum in deep learning. Sutskever, I.; Martens, J.; Dahl, G. E.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 1139-1147, 2013.
On the importance of initialization and momentum in deep learning [link] link   On the importance of initialization and momentum in deep learning [pdf] pdf   link   bibtex   2 downloads  
Modeling Natural Images Using Gated MRFs. Ranzato, M.; Mnih, V.; Susskind, J. M.; and Hinton, G. E. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 35(9): 2206-2222. 2013.
Modeling Natural Images Using Gated MRFs [link] link   Modeling Natural Images Using Gated MRFs [pdf] pdf   link   bibtex   4 downloads  
Using an autoencoder with deformable templates to discover features for automated speech recognition. Jaitly, N.; and Hinton, G. E. In Proceedings of Conference of the International Speech Communication Association (INTERSPEECH), pages 1737-1740, 2013.
Using an autoencoder with deformable templates to discover features for automated speech recognition [link] link   link   bibtex  
  2012 (18)
Robust Boltzmann Machines for recognition and denoising. Tang, Y.; Salakhutdinov, R.; and Hinton, G. E. In Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 2264-2271, 2012.
Robust Boltzmann Machines for recognition and denoising [link] link   Robust Boltzmann Machines for recognition and denoising [pdf] pdf   link   bibtex   4 downloads  
Conditional Restricted Boltzmann Machines for Structured Output Prediction. Mnih, V.; Larochelle, H.; and Hinton, G. E. CoRR, abs/1202.3748. 2012.
Conditional Restricted Boltzmann Machines for Structured Output Prediction [link] link   Conditional Restricted Boltzmann Machines for Structured Output Prediction [pdf] pdf   link   bibtex  
Deep Mixtures of Factor Analysers. Tang, Y.; Salakhutdinov, R.; and Hinton, G. E. CoRR, abs/1206.4635. 2012.
Deep Mixtures of Factor Analysers [link] link   Deep Mixtures of Factor Analysers [pdf] pdf   link   bibtex   1 download  
Efficient Parametric Projection Pursuit Density Estimation. Welling, M.; Zemel, R. S.; and Hinton, G. E. CoRR, abs/1212.2513. 2012.
Efficient Parametric Projection Pursuit Density Estimation [link] link   Efficient Parametric Projection Pursuit Density Estimation [link] psgz   link   bibtex  
Improving neural networks by preventing co-adaptation of feature detectors. Hinton, G. E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. CoRR, abs/1207.0580. 2012.
Improving neural networks by preventing co-adaptation of feature detectors [link] link   Improving neural networks by preventing co-adaptation of feature detectors [pdf] pdf   link   bibtex   5 downloads  
Products of Hidden Markov Models: It Takes N>1 to Tango. Taylor, G. W.; and Hinton, G. E. CoRR, abs/1205.2614. 2012.
Products of Hidden Markov Models: It Takes N>1 to Tango [link] link   Products of Hidden Markov Models: It Takes N>1 to Tango [pdf] pdf   link   bibtex   1 download  
Understanding how Deep Belief Networks perform acoustic modelling. Mohamed, A.; Hinton, G. E.; and Penn, G. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 4273-4276, 2012.
Understanding how Deep Belief Networks perform acoustic modelling [link] link   Understanding how Deep Belief Networks perform acoustic modelling [pdf] pdf   link   bibtex   3 downloads  
Deep Lambertian Networks. Tang, Y.; Salakhutdinov, R.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), 2012.
Deep Lambertian Networks [link] link   Deep Lambertian Networks [pdf] pdf   link   bibtex   1 download  
Deep Mixtures of Factor Analysers. Tang, Y.; Salakhutdinov, R.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), 2012.
Deep Mixtures of Factor Analysers [link] link   Deep Mixtures of Factor Analysers [pdf] pdf   link   bibtex   1 download  
Learning to Label Aerial Images from Noisy Data. Mnih, V.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), 2012.
Learning to Label Aerial Images from Noisy Data [link] link   Learning to Label Aerial Images from Noisy Data [pdf] pdf   link   bibtex  
Introduction to the Special Section on Deep Learning for Speech and Language Processing. Yu, D.; Hinton, G. E.; Morgan, N.; Chien, J.; and Sagayama, S. IEEE Transactions on Audio, Speech & Language Processing (TASLP), 20(1): 4-6. 2012.
Introduction to the Special Section on Deep Learning for Speech and Language Processing [link] link   link   bibtex   3 downloads  
Acoustic Modeling Using Deep Belief Networks. Mohamed, A.; Dahl, G. E.; and Hinton, G. E. IEEE Transactions on Audio, Speech & Language Processing (TASLP), 20(1): 14-22. 2012.
Acoustic Modeling Using Deep Belief Networks [link] link   Acoustic Modeling Using Deep Belief Networks [pdf] pdf   link   bibtex   2 downloads  
Visualizing non-metric similarities in multiple maps. van der Maaten, L.; and Hinton, G. E. Machine Learning (ML), 87(1): 33-55. 2012.
Visualizing non-metric similarities in multiple maps [link] link   Visualizing non-metric similarities in multiple maps [pdf] pdf   link   bibtex  
ImageNet Classification with Deep Convolutional Neural Networks. Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1106-1114, 2012.
ImageNet Classification with Deep Convolutional Neural Networks [pdf] link   ImageNet Classification with Deep Convolutional Neural Networks [pdf] pdf   link   bibtex   9 downloads  
A Better Way to Pretrain Deep Boltzmann Machines. Salakhutdinov, R.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 2456-2464, 2012.
A Better Way to Pretrain Deep Boltzmann Machines [pdf] link   A Better Way to Pretrain Deep Boltzmann Machines [pdf] pdf   link   bibtex  
An Efficient Learning Procedure for Deep Boltzmann Machines. Salakhutdinov, R.; and Hinton, G. E. Neural Computation (NECO), 24(8): 1967-2006. 2012.
An Efficient Learning Procedure for Deep Boltzmann Machines [link] link   An Efficient Learning Procedure for Deep Boltzmann Machines [pdf] pdf   link   bibtex  
A Practical Guide to Training Restricted Boltzmann Machines. Hinton, G. E. In Proceedings of Neural Networks: Tricks of the Trade (2nd ed.), pages 599-619. 2012.
A Practical Guide to Training Restricted Boltzmann Machines [link] link   link   bibtex  
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Hinton, G. E.; Deng, L.; Yu, D.; Dahl, G. E.; Mohamed, A.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T. N; and others Signal Processing Magazine, IEEE, 29(6): 82–97. 2012.
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  2011 (13)
Modeling the joint density of two images under a variety of transformations. Susskind, J. M.; Hinton, G. E.; Memisevic, R.; and Pollefeys, M. In Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 2793-2800, 2011.
Modeling the joint density of two images under a variety of transformations [link] link   Modeling the joint density of two images under a variety of transformations [pdf] pdf   link   bibtex  
On deep generative models with applications to recognition. Ranzato, M.; Susskind, J. M.; Mnih, V.; and Hinton, G. E. In Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 2857-2864, 2011.
On deep generative models with applications to recognition [link] link   On deep generative models with applications to recognition [pdf] pdf   link   bibtex   1 download  
A better way to learn features: technical perspective. Hinton, G. E. Commun. ACM (CACM), 54(10): 94. 2011.
A better way to learn features: technical perspective [link] link   link   bibtex   2 downloads  
Using very deep autoencoders for content-based image retrieval. Krizhevsky, A.; and Hinton, G. E. In Proceedings of The European Symposium on Artificial Neural Networks (ESANN), 2011.
Using very deep autoencoders for content-based image retrieval [pdf] link   Using very deep autoencoders for content-based image retrieval [pdf] pdf   link   bibtex   3 downloads  
Transforming Auto-Encoders. Hinton, G. E.; Krizhevsky, A.; and Wang, S. D. In Proceedings of International Conference on Artificial Neural Networks (ICANN), pages 44-51, 2011.
Transforming Auto-Encoders [link] link   Transforming Auto-Encoders [pdf] pdf   link   bibtex   2 downloads  
Deep Belief Networks using discriminative features for phone recognition. Mohamed, A.; Sainath, T. N.; Dahl, G. E.; Ramabhadran, B.; Hinton, G. E.; and Picheny, M. A. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 5060-5063, 2011.
Deep Belief Networks using discriminative features for phone recognition [link] link   Deep Belief Networks using discriminative features for phone recognition [pdf] pdf   link   bibtex   1 download  
Deep belief nets for natural language call-routing. Sarikaya, R.; Hinton, G. E.; and Ramabhadran, B. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 5680-5683, 2011.
Deep belief nets for natural language call-routing [link] link   Deep belief nets for natural language call-routing [pdf] pdf   link   bibtex   1 download  
Learning a better representation of speech soundwaves using restricted boltzmann machines. Jaitly, N.; and Hinton, G. E. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 5884-5887, 2011.
Learning a better representation of speech soundwaves using restricted boltzmann machines [link] link   Learning a better representation of speech soundwaves using restricted boltzmann machines [pdf] pdf   link   bibtex   3 downloads  
Generating Text with Recurrent Neural Networks. Sutskever, I.; Martens, J.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 1017-1024, 2011.
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Two Distributed-State Models For Generating High-Dimensional Time Series. Taylor, G. W.; Hinton, G. E.; and Roweis, S. T. Journal of Machine Learning Research (JMLR), 12: 1025-1068. 2011.
Two Distributed-State Models For Generating High-Dimensional Time Series [link] link   link   bibtex  
Conditional Restricted Boltzmann Machines for Structured Output Prediction. Mnih, V.; Larochelle, H.; and Hinton, G. E. In Proceedings of Uncertainty in Artificial Intelligence (UAI), pages 514-522, 2011.
Conditional Restricted Boltzmann Machines for Structured Output Prediction [link] link   Conditional Restricted Boltzmann Machines for Structured Output Prediction [pdf] pdf   link   bibtex   1 download  
A new way to learn acoustic events. Jaitly, N.; and Hinton, G. E. Advances in Neural Information Processing Systems, 24. 2011.
A new way to learn acoustic events [pdf] pdf   link   bibtex   2 downloads  
Discovering Binary Codes for Documents by Learning Deep Generative Models. Hinton, G.; and Salakhutdinov, R. Topics in Cognitive Science, 3(1): 74–91. 2011.
Discovering Binary Codes for Documents by Learning Deep Generative Models [link] link   doi   link   bibtex   1 download  
  2010 (18)
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images. Ranzato, M.; Krizhevsky, A.; and Hinton, G. E. In Proceedings of AISTATS, pages 621-628, 2010.
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images [link] link   Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images [pdf] pdf   link   bibtex  
Dynamical binary latent variable models for 3D human pose tracking. Taylor, G. W.; Sigal, L.; Fleet, D. J.; and Hinton, G. E. In Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 631-638, 2010.
Dynamical binary latent variable models for 3D human pose tracking [link] link   Dynamical binary latent variable models for 3D human pose tracking [pdf] pdf   link   bibtex  
Modeling pixel means and covariances using factorized third-order boltzmann machines. Ranzato, M.; and Hinton, G. E. In Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 2551-2558, 2010.
Modeling pixel means and covariances using factorized third-order boltzmann machines [link] link   Modeling pixel means and covariances using factorized third-order boltzmann machines [pdf] pdf   link   bibtex   10 downloads  
Learning to Detect Roads in High-Resolution Aerial Images. Mnih, V.; and Hinton, G. E. In Proceedings of European Conference on Computer Vision (ECCV), pages 210-223, 2010.
Learning to Detect Roads in High-Resolution Aerial Images [link] link   Learning to Detect Roads in High-Resolution Aerial Images [pdf] pdf   link   bibtex   3 downloads  
Boltzmann Machines. Hinton, G. E. In Proceedings of Encyclopedia of Machine Learning, pages 132-136. 2010.
Boltzmann Machines [link] link   link   bibtex   2 downloads  
Deep Belief Nets. Hinton, G. E. In Proceedings of Encyclopedia of Machine Learning, pages 267-269. 2010.
Deep Belief Nets [link] link   link   bibtex   1 download  
Phone recognition using Restricted Boltzmann Machines. Mohamed, A.; and Hinton, G. E. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 4354-4357, 2010.
Phone recognition using Restricted Boltzmann Machines [link] link   Phone recognition using Restricted Boltzmann Machines [pdf] pdf   link   bibtex   3 downloads  
Rectified Linear Units Improve Restricted Boltzmann Machines. Nair, V.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 807-814, 2010.
Rectified Linear Units Improve Restricted Boltzmann Machines [pdf] link   Rectified Linear Units Improve Restricted Boltzmann Machines [pdf] pdf   link   bibtex   3 downloads  
Binary coding of speech spectrograms using a deep auto-encoder. Deng, L.; Seltzer, M. L.; Yu, D.; Acero, A.; Mohamed, A.; and Hinton, G. E. In Proceedings of Conference of the International Speech Communication Association (INTERSPEECH), pages 1692-1695, 2010.
Binary coding of speech spectrograms using a deep auto-encoder [link] link   Binary coding of speech spectrograms using a deep auto-encoder [pdf] pdf   link   bibtex  
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. Dahl, G. E.; Ranzato, M.; Mohamed, A.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 469-477, 2010.
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine [pdf] link   Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine [pdf] pdf   link   bibtex   11 downloads  
Learning to combine foveal glimpses with a third-order Boltzmann machine. Larochelle, H.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1243-1251, 2010.
Learning to combine foveal glimpses with a third-order Boltzmann machine [pdf] link   Learning to combine foveal glimpses with a third-order Boltzmann machine [pdf] pdf   link   bibtex  
Gated Softmax Classification. Memisevic, R.; Zach, C.; Hinton, G. E.; and Pollefeys, M. In Proceedings of Neural Information Processing Systems (NIPS), pages 1603-1611, 2010.
Gated Softmax Classification [pdf] link   Gated Softmax Classification [pdf] pdf   link   bibtex   1 download  
Generating more realistic images using gated MRF's. Ranzato, M.; Mnih, V.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 2002-2010, 2010.
Generating more realistic images using gated MRF's [pdf] link   link   bibtex  
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines. Memisevic, R.; and Hinton, G. E. Neural Computation (NECO), 22(6): 1473-1492. 2010.
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines [link] link   Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines [pdf] pdf   link   bibtex  
Comparing Classification Methods for Longitudinal fMRI Studies. Schmah, T.; Yourganov, G.; Zemel, R. S.; Hinton, G. E.; Small, S. L.; and Strother, S. C. Neural Computation (NECO), 22(11): 2729-2762. 2010.
Comparing Classification Methods for Longitudinal fMRI Studies [link] link   link   bibtex   1 download  
Temporal-Kernel Recurrent Neural Networks. Sutskever, I.; and Hinton, G. E. Neural Networks (NN), 23(2): 239-243. 2010.
Temporal-Kernel Recurrent Neural Networks [link] link   link   bibtex  
Generating more realistic images using gated MRF's. Ranzato, M.; Mnih, V.; and Hinton, G. E. In Lafferty, J. D.; Williams, C. K. I.; Shawe-Taylor, J.; Zemel, R. S.; and Culotta, A., editor(s), NIPS, pages 2002-2010, 2010. Curran Associates, Inc.
Generating more realistic images using gated MRF's. [pdf]Link   Generating more realistic images using gated MRF's. [link] link   Generating more realistic images using gated MRF's. [pdf] pdf   link   bibtex   1 download  
Learning to represent visual input. Hinton, G. E. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1537): 177–184. January 2010.
Learning to represent visual input [link] link   Learning to represent visual input [pdf] pdf   doi   link   bibtex   abstract   1 download  
  2009 (14)
Deep Boltzmann Machines. Salakhutdinov, R.; and Hinton, G. E. In Proceedings of AISTATS, pages 448-455, 2009.
Deep Boltzmann Machines [link] link   Deep Boltzmann Machines [pdf] pdf   link   bibtex   3 downloads  
Learning Generative Texture Models with extended Fields-of-Experts. Heess, N.; Williams, C. K. I.; and Hinton, G. E. In Proceedings of British Machine Vision Conference (BMVC), pages 1-11, 2009.
Learning Generative Texture Models with extended Fields-of-Experts [link] link   Learning Generative Texture Models with extended Fields-of-Experts [pdf] pdf   link   bibtex  
Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine. Zeiler, M. D.; Taylor, G. W.; Troje, N. F.; and Hinton, G. E. In Proceedings of The European Symposium on Artificial Neural Networks (ESANN), 2009.
Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine [pdf] link   Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine [pdf] pdf   link   bibtex  
Factored conditional restricted Boltzmann Machines for modeling motion style. Taylor, G. W.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 129, 2009.
Factored conditional restricted Boltzmann Machines for modeling motion style [link] link   Factored conditional restricted Boltzmann Machines for modeling motion style [pdf] pdf   link   bibtex  
Using fast weights to improve persistent contrastive divergence. Tieleman, T.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 130, 2009.
Using fast weights to improve persistent contrastive divergence [link] link   Using fast weights to improve persistent contrastive divergence [pdf] pdf   link   bibtex  
Workshop summary: Workshop on learning feature hierarchies. Yu, K.; Salakhutdinov, R.; LeCun, Y.; Hinton, G. E.; and Bengio, Y. In Proceedings of International Conference on Machine Learning (ICML), pages 165, 2009.
Workshop summary: Workshop on learning feature hierarchies [link] link   link   bibtex  
Semantic hashing. Salakhutdinov, R.; and Hinton, G. E. Int. J. Approx. Reasoning (IJAR), 50(7): 969-978. 2009.
Semantic hashing [link] link   Semantic hashing [pdf] pdf   link   bibtex   2 downloads  
3D Object Recognition with Deep Belief Nets. Nair, V.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1339-1347, 2009.
3D Object Recognition with Deep Belief Nets [pdf] link   3D Object Recognition with Deep Belief Nets [pdf] pdf   link   bibtex   1 download  
Zero-shot Learning with Semantic Output Codes. Palatucci, M.; Pomerleau, D.; Hinton, G. E.; and Mitchell, T. M. In Proceedings of Neural Information Processing Systems (NIPS), pages 1410-1418, 2009.
Zero-shot Learning with Semantic Output Codes [pdf] link   Zero-shot Learning with Semantic Output Codes [pdf] pdf   link   bibtex  
Replicated Softmax: an Undirected Topic Model. Salakhutdinov, R.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1607-1614, 2009.
Replicated Softmax: an Undirected Topic Model [pdf] link   Replicated Softmax: an Undirected Topic Model [pdf] pdf   link   bibtex  
Improving a statistical language model through non-linear prediction. Mnih, A.; Yuecheng, Z.; and Hinton, G. E. Neurocomputing (IJON), 72(7-9): 1414-1418. 2009.
Improving a statistical language model through non-linear prediction [link] link   link   bibtex   2 downloads  
Deep belief networks. Hinton, G. E. Scholarpedia, 4(5): 5947. 2009.
Deep belief networks [link] link   link   bibtex  
Products of Hidden Markov Models: It Takes N>1 to Tango. Taylor, G. W.; and Hinton, G. E. In Proceedings of Uncertainty in Artificial Intelligence (UAI), pages 522-529, 2009.
Products of Hidden Markov Models: It Takes N>1 to Tango [link] link   link   bibtex  
Deep Belief Networks for phone recognition. Mohamed, A.; Dahl, G.; and Hinton, G. NIPS 22 workshop on deep learning for speech recognition. 2009.
Deep Belief Networks for phone recognition [pdf] pdf   link   bibtex   4 downloads  
  2008 (10)
Improving a statistical language model by modulating the effects of context words. Yuecheng, Z.; Mnih, A.; and Hinton, G. E. In Proceedings of The European Symposium on Artificial Neural Networks (ESANN), pages 493-498, 2008.
Improving a statistical language model by modulating the effects of context words [pdf] link   Improving a statistical language model by modulating the effects of context words [pdf] pdf   link   bibtex  
Analysis-by-Synthesis by Learning to Invert Generative Black Boxes. Nair, V.; Susskind, J. M.; and Hinton, G. E. In Proceedings of International Conference on Artificial Neural Networks (ICANN), pages 971-981, 2008.
Analysis-by-Synthesis by Learning to Invert Generative Black Boxes [link] link   Analysis-by-Synthesis by Learning to Invert Generative Black Boxes [pdf] pdf   link   bibtex   1 download  
A Scalable Hierarchical Distributed Language Model. Mnih, A.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1081-1088, 2008.
A Scalable Hierarchical Distributed Language Model [pdf] link   A Scalable Hierarchical Distributed Language Model [pdf] pdf   link   bibtex  
Implicit Mixtures of Restricted Boltzmann Machines. Nair, V.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1145-1152, 2008.
Implicit Mixtures of Restricted Boltzmann Machines [pdf] link   Implicit Mixtures of Restricted Boltzmann Machines [pdf] pdf   link   bibtex  
Generative versus discriminative training of RBMs for classification of fMRI images. Schmah, T.; Hinton, G. E.; Zemel, R. S.; Small, S. L.; and Strother, S. C. In Proceedings of Neural Information Processing Systems (NIPS), pages 1409-1416, 2008.
Generative versus discriminative training of RBMs for classification of fMRI images [pdf] link   Generative versus discriminative training of RBMs for classification of fMRI images [pdf] pdf   link   bibtex  
Using matrices to model symbolic relationship. Sutskever, I.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1593-1600, 2008.
Using matrices to model symbolic relationship [pdf] link   Using matrices to model symbolic relationship [pdf] pdf   link   bibtex   1 download  
The Recurrent Temporal Restricted Boltzmann Machine. Sutskever, I.; Hinton, G. E.; and Taylor, G. W. In Proceedings of Neural Information Processing Systems (NIPS), pages 1601-1608, 2008.
The Recurrent Temporal Restricted Boltzmann Machine [pdf] link   The Recurrent Temporal Restricted Boltzmann Machine [pdf] pdf   link   bibtex  
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators. Sutskever, I.; and Hinton, G. E. Neural Computation (NECO), 20(11): 2629-2636. 2008.
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators [link] link   Deep, Narrow Sigmoid Belief Networks Are Universal Approximators [pdf] pdf   link   bibtex   1 download  
Visualizing Data using t-SNE. Van der Maaten, L.; and Hinton, G. Journal of Machine Learning Research, 9(11). 2008.
Visualizing Data using t-SNE. [pdf] pdf   Visualizing Data using t-SNE. [pdf] supplementary material 25mb   link   bibtex  
Generating facial expressions with deep belief nets. Susskind, J. M.; Hinton, G. E.; Movellan, J. R.; and Anderson, A. K. Affective Computing, Emotion Modelling, Synthesis and Recognition,421–440. 2008.
Generating facial expressions with deep belief nets [pdf] pdf   link   bibtex   1 download  
  2007 (11)
Visualizing Similarity Data with a Mixture of Maps. Cook, J.; Sutskever, I.; Mnih, A.; and Hinton, G. E. In Proceedings of AISTATS, pages 67-74, 2007.
Visualizing Similarity Data with a Mixture of Maps [link] link   Visualizing Similarity Data with a Mixture of Maps [pdf] pdf   link   bibtex  
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. Salakhutdinov, R.; and Hinton, G. E. In Proceedings of AISTATS, pages 412-419, 2007.
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure [link] link   Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure [pdf] pdf   link   bibtex  
Learning Multilevel Distributed Representations for High-Dimensional Sequences. Sutskever, I.; and Hinton, G. E. In Proceedings of AISTATS, pages 548-555, 2007.
Learning Multilevel Distributed Representations for High-Dimensional Sequences [link] link   Learning Multilevel Distributed Representations for High-Dimensional Sequences [pdf] pdf   link   bibtex  
Unsupervised Learning of Image Transformations. Memisevic, R.; and Hinton, G. E. In Proceedings of Computer Vision and Pattern Recognition (CVPR), 2007.
Unsupervised Learning of Image Transformations [link] link   Unsupervised Learning of Image Transformations [pdf] pdf   link   bibtex  
Three new graphical models for statistical language modelling. Mnih, A.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 641-648, 2007.
Three new graphical models for statistical language modelling [link] link   Three new graphical models for statistical language modelling [pdf] pdf   link   bibtex  
Restricted Boltzmann machines for collaborative filtering. Salakhutdinov, R.; Mnih, A.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 791-798, 2007.
Restricted Boltzmann machines for collaborative filtering [link] link   Restricted Boltzmann machines for collaborative filtering [pdf] pdf   link   bibtex   2 downloads  
Modeling image patches with a directed hierarchy of Markov random fields. Osindero, S.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), 2007.
Modeling image patches with a directed hierarchy of Markov random fields [pdf] link   Modeling image patches with a directed hierarchy of Markov random fields [pdf] pdf   link   bibtex  
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. Salakhutdinov, R.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), 2007.
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes [pdf] link   Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes [pdf] pdf   link   bibtex  
Boltzmann machine. Hinton, G. E. Scholarpedia, 2(5): 1668. 2007.
Boltzmann machine [link] link   link   bibtex  
Learning multiple layers of representation. Hinton, G. E. Trends in Cognitive Sciences, 11(10). 2007.
Learning multiple layers of representation [pdf] pdf   link   bibtex   abstract   1 download  
To Recognize Shapes First Learn to Generate Images. Hinton, G. E. In Computational Neuroscience: Theoretical Insights into Brain Function, 2007. Elsevier
To Recognize Shapes First Learn to Generate Images [pdf] draft   link   bibtex   abstract  
  2006 (5)
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation. Hinton, G. E.; Osindero, S.; Welling, M.; and Teh, Y. W. Cognitive Science (COGSCI), 30(4): 725-731. 2006.
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation [link] link   Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation [ps] ps   Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation [pdf] pdf   link   bibtex  
Modeling Human Motion Using Binary Latent Variables. Taylor, G. W.; Hinton, G. E.; and Roweis, S. T. In Proceedings of Neural Information Processing Systems (NIPS), pages 1345-1352, 2006.
Modeling Human Motion Using Binary Latent Variables [pdf] link   Modeling Human Motion Using Binary Latent Variables [pdf] pdf   link   bibtex   1 download  
Topographic Product Models Applied to Natural Scene Statistics. Osindero, S.; Welling, M.; and Hinton, G. E. Neural Computation (NECO), 18(2): 381-414. 2006.
Topographic Product Models Applied to Natural Scene Statistics [link] link   Topographic Product Models Applied to Natural Scene Statistics [pdf] pdf   link   bibtex  
A Fast Learning Algorithm for Deep Belief Nets. Hinton, G. E.; Osindero, S.; and Teh, Y. W. Neural Computation (NECO), 18(7): 1527-1554. 2006.
A Fast Learning Algorithm for Deep Belief Nets [link] link   A Fast Learning Algorithm for Deep Belief Nets [pdf] pdf   link   bibtex   4 downloads  
Reducing the dimensionality of data with neural networks. Hinton, G. E.; and Salakhutdinov, R. R. Science, 313(5786): 504-507. July 2006.
Reducing the dimensionality of data with neural networks [link] link   Reducing the dimensionality of data with neural networks [link] abstract   Reducing the dimensionality of data with neural networks [pdf] paper   Reducing the dimensionality of data with neural networks [pdf] supporting online material   Reducing the dimensionality of data with neural networks [link] matlab code   doi   link   bibtex   abstract   14 downloads  
  2005 (6)
What kind of graphical model is the brain?. Hinton, G. E. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 1765-, 2005.
What kind of graphical model is the brain? [pdf] link   What kind of graphical model is the brain? [link] psgz   What kind of graphical model is the brain? [pdf] pdf   link   bibtex   2 downloads  
Inferring Motor Programs from Images of Handwritten Digits. Hinton, G. E.; and Nair, V. In Proceedings of Neural Information Processing Systems (NIPS), 2005.
Inferring Motor Programs from Images of Handwritten Digits [pdf] link   Inferring Motor Programs from Images of Handwritten Digits [link] psgz   Inferring Motor Programs from Images of Handwritten Digits [pdf] pdf   link   bibtex   1 download  
Improving dimensionality reduction with spectral gradient descent. Memisevic, R.; and Hinton, G. E. Neural Networks (NN), 18(5-6): 702-710. 2005.
Improving dimensionality reduction with spectral gradient descent [link] link   link   bibtex  
Learning causally linked markov random fields. Hinton, G. E.; Osindero, S.; and Bao, K. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pages 128–135, 2005.
Learning causally linked markov random fields [link] psgz   Learning causally linked markov random fields [pdf] pdf   link   bibtex   4 downloads  
Learning Unreliable Constraints using Contrastive Divergence. Mnih, A.; and E., H. G. In Proceedings of the International Joint Conference on Neural Networks, Montreal, 2005.
Learning Unreliable Constraints using Contrastive Divergence [link] psgz   Learning Unreliable Constraints using Contrastive Divergence [pdf] pdf   link   bibtex  
On contrastive divergence learning. Carreira-Perpinan, M. A.; and Hinton, G. E. In Proceedings of the tenth international workshop on artificial intelligence and statistics, pages 33–40, 2005. Society for Artificial Intelligence and Statistics NP
On contrastive divergence learning [pdf] pdf   link   bibtex   2 downloads  
  2004 (6)
Probabilistic sequential independent components analysis. Welling, M.; Zemel, R. S.; and Hinton, G. E. IEEE Transactions on Neural Networks (TNN), 15(4): 838-849. 2004.
Probabilistic sequential independent components analysis [link] link   Probabilistic sequential independent components analysis [link] psgz   Probabilistic sequential independent components analysis [pdf] pdf   link   bibtex   1 download  
Reinforcement Learning with Factored States and Actions. Sallans, B.; and Hinton, G. E. Journal of Machine Learning Research (JMLR), 5: 1063-1088. 2004.
Reinforcement Learning with Factored States and Actions [pdf]Paper   link   bibtex   1 download  
Exponential Family Harmoniums with an Application to Information Retrieval. Welling, M.; Rosen-Zvi, M.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), 2004.
Exponential Family Harmoniums with an Application to Information Retrieval [pdf] link   Exponential Family Harmoniums with an Application to Information Retrieval [link] psgz   Exponential Family Harmoniums with an Application to Information Retrieval [pdf] pdf   link   bibtex   2 downloads  
Multiple Relational Embedding. Memisevic, R.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), 2004.
Multiple Relational Embedding [pdf] link   Multiple Relational Embedding [link] psgz   Multiple Relational Embedding [pdf] pdf   link   bibtex  
Neighbourhood Components Analysis. Goldberger, J.; Roweis, S. T.; Hinton, G. E.; and Salakhutdinov, R. In Proceedings of Neural Information Processing Systems (NIPS), 2004.
Neighbourhood Components Analysis [pdf] link   Neighbourhood Components Analysis [pdf] pdf   link   bibtex   1 download  
Distinguishing text from graphics in on-line handwritten ink. Bishop, C. M.; Svensén, M.; and Hinton, G. E. In IWFHR, pages 142-147, 2004. IEEE Computer Society
Distinguishing text from graphics in on-line handwritten ink. [link]Link   Distinguishing text from graphics in on-line handwritten ink. [link] link   Distinguishing text from graphics in on-line handwritten ink. [pdf] pdf   link   bibtex  
  2003 (4)
Energy-Based Models for Sparse Overcomplete Representations. Teh, Y. W.; Welling, M.; Osindero, S.; and Hinton, G. E. Journal of Machine Learning Research (JMLR), 4: 1235-1260. 2003.
Energy-Based Models for Sparse Overcomplete Representations [link] link   Energy-Based Models for Sparse Overcomplete Representations [link] psgz   Energy-Based Models for Sparse Overcomplete Representations [pdf] pdf   link   bibtex   1 download  
Wormholes Improve Contrastive Divergence. Hinton, G. E.; Welling, M.; and Mnih, A. In Proceedings of Neural Information Processing Systems (NIPS), 2003.
Wormholes Improve Contrastive Divergence [pdf] link   Wormholes Improve Contrastive Divergence [link] psgz   Wormholes Improve Contrastive Divergence [pdf] pdf   link   bibtex  
Efficient Parametric Projection Pursuit Density Estimation. Welling, M.; Zemel, R. S.; and Hinton, G. E. In Proceedings of Uncertainty in Artificial Intelligence (UAI), pages 575-582, 2003.
Efficient Parametric Projection Pursuit Density Estimation [link] link   Efficient Parametric Projection Pursuit Density Estimation [link] psgz   link   bibtex  
The Ups and Downs of Hebb Synapses. Hinton, G. E. Canadian Psychology/Psychologie canadienne, 44(1): 10-13. 2003.
The Ups and Downs of Hebb Synapses [pdf] pdf   link   bibtex  
  2002 (11)
Recognizing handwritten digits using hierarchical products of experts. Mayraz, G.; and Hinton, G. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(2): 189-197. Feb 2002.
doi   link   bibtex   abstract  
In Memory of Ray Reiter (1939-2002). Pirri, F.; Hinton, G. E.; and Levesque, H. J. AI Magazine (AIM), 23(4): 93. 2002.
In Memory of Ray Reiter (1939-2002) [link] link   link   bibtex  
Local Physical Models for Interactive Character Animation. Oore, S.; Terzopoulos, D.; and Hinton, G. E. Comput. Graph. Forum (CGF), 21(3): 337-346. 2002.
Local Physical Models for Interactive Character Animation [link] link   Local Physical Models for Interactive Character Animation [pdf] pdf   link   bibtex  
A Desktop Input Device and Interface for Interactive 3D Character Animation. Oore, S.; Terzopoulos, D.; and Hinton, G. E. In Proceedings of Graphics Interface, pages 133-140, 2002.
A Desktop Input Device and Interface for Interactive 3D Character Animation [link] link   A Desktop Input Device and Interface for Interactive 3D Character Animation [pdf] pdf   link   bibtex  
A New Learning Algorithm for Mean Field Boltzmann Machines. Welling, M.; and Hinton, G. E. In Proceedings of International Conference on Artificial Neural Networks (ICANN), pages 351-357, 2002.
A New Learning Algorithm for Mean Field Boltzmann Machines [link] link   A New Learning Algorithm for Mean Field Boltzmann Machines [pdf] pdf   link   bibtex   1 download  
Recognizing Handwritten Digits Using Hierarchical Products of Experts. Mayraz, G.; and Hinton, G. E. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 24(2): 189-197. 2002.
Recognizing Handwritten Digits Using Hierarchical Products of Experts [link] link   Recognizing Handwritten Digits Using Hierarchical Products of Experts [pdf] pdf   link   bibtex  
Self Supervised Boosting. Welling, M.; Zemel, R. S.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 665-672, 2002.
Self Supervised Boosting [pdf] link   Self Supervised Boosting [pdf] pdf   Self Supervised Boosting [link] psgz   link   bibtex  
Stochastic Neighbor Embedding. Hinton, G. E.; and Roweis, S. T. In Proceedings of Neural Information Processing Systems (NIPS), pages 833-840, 2002.
Stochastic Neighbor Embedding [pdf] link   Stochastic Neighbor Embedding [link] psgz   Stochastic Neighbor Embedding [pdf] pdf   link   bibtex  
Learning Sparse Topographic Representations with Products of Student-t Distributions. Welling, M.; Hinton, G. E.; and Osindero, S. In Proceedings of Neural Information Processing Systems (NIPS), pages 1359-1366, 2002.
Learning Sparse Topographic Representations with Products of Student-t Distributions [pdf] link   Learning Sparse Topographic Representations with Products of Student-t Distributions [link] psgz   Learning Sparse Topographic Representations with Products of Student-t Distributions [pdf] pdf   link   bibtex  
Training Products of Experts by Minimizing Contrastive Divergence. Hinton, G. E. Neural Computation (NECO), 14(8): 1771-1800. 2002.
Training Products of Experts by Minimizing Contrastive Divergence [link] link   Training Products of Experts by Minimizing Contrastive Divergence [pdf] pdf   link   bibtex   2 downloads  
Classical and Bayesian inference in neuroimaging: theory. Friston, K. J.; Penny, W.; Phillips, C.; Kiebel, S.; Hinton, G.; and Ashburner, J. NeuroImage, 16(2): 465–483. 2002.
Classical and Bayesian inference in neuroimaging: theory [pdf] pdf   link   bibtex  
  2001 (9)
Learning Distributed Representations of Concepts Using Linear Relational Embedding. Paccanaro, A.; and Hinton, G. E. IEEE Trans. Knowl. Data Eng. (TKDE), 13(2): 232-244. 2001.
Learning Distributed Representations of Concepts Using Linear Relational Embedding [link] link   Learning Distributed Representations of Concepts Using Linear Relational Embedding [link] abstract   Learning Distributed Representations of Concepts Using Linear Relational Embedding [link] psgz   Learning Distributed Representations of Concepts Using Linear Relational Embedding [pdf] pdf   link   bibtex   1 download  
Learning Hierarchical Structures with Linear Relational Embedding. Paccanaro, A.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 857-864, 2001.
Learning Hierarchical Structures with Linear Relational Embedding [link] link   Learning Hierarchical Structures with Linear Relational Embedding [pdf] pdf   Learning Hierarchical Structures with Linear Relational Embedding [ps] ps   Learning Hierarchical Structures with Linear Relational Embedding [link] psgz   link   bibtex   1 download  
Global Coordination of Local Linear Models. Roweis, S. T.; Saul, L. K.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 889-896, 2001.
Global Coordination of Local Linear Models [link] link   Global Coordination of Local Linear Models [link] psgz   Global Coordination of Local Linear Models [pdf] pdf   link   bibtex  
Relative Density Nets: A New Way to Combine Backpropagation with HMM's. Brown, A. D.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1149-1156, 2001.
Relative Density Nets: A New Way to Combine Backpropagation with HMM's [link] link   link   bibtex  
Discovering Multiple Constraints that are Frequently Approximately Satisfied. Hinton, G. E.; and Teh, Y. W. In Proceedings of Uncertainty in Artificial Intelligence (UAI), pages 227-234, 2001.
Discovering Multiple Constraints that are Frequently Approximately Satisfied [link] link   Discovering Multiple Constraints that are Frequently Approximately Satisfied [link] psgz   Discovering Multiple Constraints that are Frequently Approximately Satisfied [pdf] pdf   link   bibtex  
Relative Density Nets: A New Way to Combine Backpropagation with HMM's. Brown, A. D.; and Hinton, G. E. In Dietterich, T. G.; Becker, S.; and Ghahramani, Z., editor(s), NIPS, pages 1149-1156, 2001. MIT Press
Relative Density Nets: A New Way to Combine Backpropagation with HMM's. [link]Link   Relative Density Nets: A New Way to Combine Backpropagation with HMM's. [link] link   Relative Density Nets: A New Way to Combine Backpropagation with HMM's. [pdf] pdf   Relative Density Nets: A New Way to Combine Backpropagation with HMM's. [ps] ps   Relative Density Nets: A New Way to Combine Backpropagation with HMM's. [link] psgz   link   bibtex  
A new view of ICA. Hinton, G. E.; Welling, M.; Teh, Y. W.; and Osindero, S. In Int. Conf. on Independent Component Analysis and Blind Source Separation, 2001.
A new view of ICA [link] psgz   A new view of ICA [pdf] pdf   link   bibtex  
Training many small hidden markov models. Hinton, G.; and Brown, A. . 2001.
Training many small hidden markov models [ps] ps   Training many small hidden markov models [ps] psgz   Training many small hidden markov models [pdf] pdf   link   bibtex  
Products of hidden markov models. Brown, A. D.; and Hinton, G. E. In In Proceedings of Artificial Intelligence and Statistics, 2001.
Products of hidden markov models [link] abstract   Products of hidden markov models [ps] ps   Products of hidden markov models [ps] psgz   Products of hidden markov models [pdf] pdf   link   bibtex  
  2000 (9)
Modeling High-Dimensional Data by Combining Simple Experts. Hinton, G. E. In Proceedings of AAAI/IAAI, pages 1159-1164, 2000.
Modeling High-Dimensional Data by Combining Simple Experts [link] link   Modeling High-Dimensional Data by Combining Simple Experts [pdf] pdf   link   bibtex  
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space. Paccanaro, A.; and Hinton, G. E. In Proceedings of International Conference on Machine Learning (ICML), pages 711-718, 2000.
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space [ps] ps   Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space [link] psgz   Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space [pdf] pdf   link   bibtex   1 download  
Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions. Paccanaro, A.; and Hinton, G. E. In Proceedings of International Joint Conference on Neural Network (IJCNN), pages 259-264, 2000.
Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions [link] link   Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions [pdf] pdf   Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions [ps] ps   Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions [link] psgz   link   bibtex  
Rate-coded Restricted Boltzmann Machines for Face Recognition. Teh, Y. W.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 908-914, 2000.
Rate-coded Restricted Boltzmann Machines for Face Recognition [link] abstract   Rate-coded Restricted Boltzmann Machines for Face Recognition [link] psgz   Rate-coded Restricted Boltzmann Machines for Face Recognition [pdf] pdf   link   bibtex  
Recognizing Hand-written Digits Using Hierarchical Products of Experts. Mayraz, G.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 953-959, 2000.
Recognizing Hand-written Digits Using Hierarchical Products of Experts [pdf] pdf   link   bibtex  
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task. Sallans, B.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 1075-1081, 2000.
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task [link] abstract   Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task [link] psgz   Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task [pdf] pdf   link   bibtex  
Variational Learning for Switching State-Space Models. Ghahramani, Z.; and Hinton, G. E. Neural Computation (NECO), 12(4): 831-864. 2000.
Variational Learning for Switching State-Space Models [link] link   Variational Learning for Switching State-Space Models [link] abstract   Variational Learning for Switching State-Space Models [link] psgz   Variational Learning for Switching State-Space Models [pdf] pdf   link   bibtex  
SMEM Algorithm for Mixture Models. Ueda, N.; Nakano, R.; Ghahramani, Z.; and Hinton, G. E. Neural Computation (NECO), 12(9): 2109-2128. 2000.
SMEM Algorithm for Mixture Models [link] link   SMEM Algorithm for Mixture Models [link] abstract   SMEM Algorithm for Mixture Models [link] psgz   SMEM Algorithm for Mixture Models [pdf] pdf   link   bibtex  
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. Ueda, N.; Nakano, R.; Ghahramani, Z.; and Hinton, G. E. VLSI Signal Processing (VLSISP), 26(1-2): 133-140. 2000.
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates [link] link   link   bibtex  
  1999 (9)
Spiking Boltzmann Machines. Hinton, G. E.; and Brown, A. D. In Proceedings of Neural Information Processing Systems (NIPS), pages 122-128, 1999.
Spiking Boltzmann Machines [link] link   Spiking Boltzmann Machines [link] abstract   Spiking Boltzmann Machines [link] psgz   Spiking Boltzmann Machines [pdf] pdf   link   bibtex   1 download  
Learning to Parse Images. Hinton, G. E.; Ghahramani, Z.; and Teh, Y. W. In Proceedings of Neural Information Processing Systems (NIPS), pages 463-469, 1999.
Learning to Parse Images [link] link   Learning to Parse Images [link] abstract   Learning to Parse Images [link] psgz   Learning to Parse Images [pdf] pdf   link   bibtex  
Variational Learning in Nonlinear Gaussian Belief Networks. Frey, B. J.; and Hinton, G. E. Neural Computation (NECO), 11(1): 193-213. 1999.
Variational Learning in Nonlinear Gaussian Belief Networks [link] link   Variational Learning in Nonlinear Gaussian Belief Networks [link] abstract   Variational Learning in Nonlinear Gaussian Belief Networks [ps] ps   Variational Learning in Nonlinear Gaussian Belief Networks [pdf] pdf   link   bibtex  
A view of the EM algorithm that justifies incremental, sparse, and other variants. Neal, R.; and Hinton, G. E. In Jordan, M. I., editor(s), Learning in Graphical Models, pages 355–368. MIT Press, 1999.
A view of the EM algorithm that justifies incremental, sparse, and other variants [pdf] link   A view of the EM algorithm that justifies incremental, sparse, and other variants [link] abstract   A view of the EM algorithm that justifies incremental, sparse, and other variants [ps] ps   A view of the EM algorithm that justifies incremental, sparse, and other variants [pdf] pdf   link   bibtex   5 downloads  
Supervised learning in multilayer neural networks. Hinton, G. The MIT encyclopaedia of the cognitive sciences,814–816. 1999.
Supervised learning in multilayer neural networks [ps] ps   Supervised learning in multilayer neural networks [pdf] pdf   link   bibtex   2 downloads  
Training Products of Experts by maximizing contrastive likelihood. Hinton, G. E. In Tech. Rep., Gatsby Computational Neuroscience Unit, 1999.
Training Products of Experts by maximizing contrastive likelihood [link] link   link   bibtex   1 download  
Products of experts. Hinton, G. E. In Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470), volume 1, pages 1–6, 1999. IET
Products of experts [link] abstract   Products of experts [ps] ps   Products of experts [pdf] pdf   link   bibtex   1 download  
Scaling in a hierarchical unsupervised network. Ghahramani, Z.; Korenberg, A. T.; and Hinton, G. E. In Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470), volume 1, pages 13–18, 1999. IET
Scaling in a hierarchical unsupervised network [link] abstract   Scaling in a hierarchical unsupervised network [link] psgz   Scaling in a hierarchical unsupervised network [pdf] pdf   link   bibtex  
Unsupervised learning: foundations of neural computation. Hinton, G. E.; and Sejnowski, T. J. MIT press, 1999.
link   bibtex  
  1998 (8)
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls. Fels, S. S.; and Hinton, G. E. IEEE Transactions on Neural Networks (TNN), 9(1): 205-212. 1998.
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls [link] link   Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls [link] abstract   Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls [ps] ps   Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls [pdf] pdf   link   bibtex  
SMEM Algorithm for Mixture Models. Ueda, N.; Nakano, R.; Ghahramani, Z.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 599-605, 1998.
SMEM Algorithm for Mixture Models [link] link   SMEM Algorithm for Mixture Models [link] abstract   SMEM Algorithm for Mixture Models [link] psgz   SMEM Algorithm for Mixture Models [pdf] pdf   link   bibtex  
Fast Neural Network Emulation of Dynamical Systems for Computer Animation. Grzeszczuk, R.; Terzopoulos, D.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 882-888, 1998.
Fast Neural Network Emulation of Dynamical Systems for Computer Animation [link] link   link   bibtex  
NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models. Grzeszczuk, R.; Terzopoulos, D.; and Hinton, G. E. In Proceedings of International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pages 9-20, 1998.
NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models [link] link   NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models [pdf] pdf   link   bibtex  
Cascaded redundancy reduction. De Sa, V. R.; and Hinton, G. E. Network: Computation in Neural Systems, 9(1): 73–84. 1998.
Cascaded redundancy reduction [link] abstract   Cascaded redundancy reduction [ps] ps   Cascaded redundancy reduction [pdf] pdf   link   bibtex  
Coaching variables for regression and classification. Tibshirani, R.; and Hinton, G. Statistics and Computing, 8(1): 25–33. 1998.
Coaching variables for regression and classification [link] abstract   Coaching variables for regression and classification [ps] ps   Coaching variables for regression and classification [pdf] pdf   link   bibtex  
A comparison of statistical learning methods on the GUSTO database. Ennis, M.; Hinton, G.; Naylor, D.; Revow, M.; and Tibshirani, R. Statistics in medicine, 17(21): 2501–2508. 1998.
A comparison of statistical learning methods on the GUSTO database [pdf] pdf   link   bibtex  
A hierarchical community of experts. Hinton, G. E.; Sallans, B.; and Ghahramani, Z. In Learning in graphical models, pages 479–494. Springer, 1998.
A hierarchical community of experts [link] abstract   A hierarchical community of experts [link] psgz   A hierarchical community of experts [pdf] pdf   link   bibtex  
  1997 (12)
Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model. Frey, B. J.; and Hinton, G. E. Comput. J. (CJ), 40(2/3): 157-165. 1997.
Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model [link] abstract   Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model [ps] ps   Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model [pdf] pdf   link   bibtex  
Instantiating Deformable Models with a Neural Net. Williams, C. K. I.; Revow, M.; and Hinton, G. E. Computer Vision and Image Understanding (CVIU), 68(1): 120-126. 1997.
Instantiating Deformable Models with a Neural Net [link] link   Instantiating Deformable Models with a Neural Net [link] abstract   Instantiating Deformable Models with a Neural Net [ps] ps   Instantiating Deformable Models with a Neural Net [pdf] pdf   link   bibtex  
Modeling the manifolds of images of handwritten digits. Hinton, G. E.; Dayan, P.; and Revow, M. IEEE Transactions on Neural Networks (TNN), 8(1): 65-74. 1997.
Modeling the manifolds of images of handwritten digits [link] link   Modeling the manifolds of images of handwritten digits [link] abstract   Modeling the manifolds of images of handwritten digits [ps] ps   Modeling the manifolds of images of handwritten digits [link] psgz   Modeling the manifolds of images of handwritten digits [pdf] pdf   link   bibtex   1 download  
Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls. Fels, S. S.; and Hinton, G. E. IEEE Transactions on Neural Networks (TNN), 8(5): 977-984. 1997.
Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls [link] link   Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls [link] abstract   Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls [ps] ps   Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls [pdf] pdf   link   bibtex  
Hierarchical Non-linear Factor Analysis and Topographic Maps. Ghahramani, Z.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), 1997.
Hierarchical Non-linear Factor Analysis and Topographic Maps [link] abstract   Hierarchical Non-linear Factor Analysis and Topographic Maps [link] psgz   Hierarchical Non-linear Factor Analysis and Topographic Maps [pdf] pdf   link   bibtex   2 downloads  
Using Expectation-Maximization for Reinforcement Learning. Dayan, P.; and Hinton, G. E. Neural Computation (NECO), 9(2): 271-278. 1997.
Using Expectation-Maximization for Reinforcement Learning [link] link   Using Expectation-Maximization for Reinforcement Learning [link] abstract   Using Expectation-Maximization for Reinforcement Learning [link] psgz   Using Expectation-Maximization for Reinforcement Learning [pdf] pdf   link   bibtex  
A Mobile Robot that Learns its Place. Oore, S.; Hinton, G. E.; and Dudek, G. Neural Computation (NECO), 9(3): 683-699. 1997.
A Mobile Robot that Learns its Place [link] link   A Mobile Robot that Learns its Place [link] abstract   A Mobile Robot that Learns its Place [ps] ps   A Mobile Robot that Learns its Place [pdf] pdf   link   bibtex  
Generative Models for Discovering Sparse Distributed Representations. Hinton, G. E.; and Ghahramani, Z. Technical Report University of Toronto, Department of Computer Science, Toronto, Ontario, M5S 1A4, Canada, 1997.
Generative Models for Discovering Sparse Distributed Representations [link] abstract   Generative Models for Discovering Sparse Distributed Representations [ps] ps   Generative Models for Discovering Sparse Distributed Representations [pdf] pdf   link   bibtex   2 downloads  
Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation. Hinton, G. E.; and Revow, M. . 1997.
Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation [link] abstract   Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation [ps] ps   Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation [link] psgz   Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation [pdf] pdf   link   bibtex  
Minimizing description length in an unsupervised neural network. Hinton, G. E.; and Zemel, R. S. . 1997. Preprint
Minimizing description length in an unsupervised neural network [link] abstract   Minimizing description length in an unsupervised neural network [ps] ps   Minimizing description length in an unsupervised neural network [pdf] pdf   link   bibtex  
GTM through time. Bishop, C.; Hinton, G.; and Strachan, I. In Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440), pages 111–116, 1997. IET
GTM through time [pdf] pdf   link   bibtex  
A simple algorithm that discovers efficient perceptual codes. Frey, B. J.; Dayan, P.; and Hinton, G. E. Computational and psychophysical mechanisms of visual coding,296–315. 1997.
A simple algorithm that discovers efficient perceptual codes [link] abstract   A simple algorithm that discovers efficient perceptual codes [ps] ps   A simple algorithm that discovers efficient perceptual codes [pdf] pdf   link   bibtex  
  1996 (6)
Free Energy Coding. Frey, B. J.; and Hinton, G. E. In Proceedings of Data Compression Conference, pages 73-81, 1996.
Free Energy Coding [link] link   link   bibtex   1 download  
Using Generative Models for Handwritten Digit Recognition. Revow, M.; Williams, C. K. I.; and Hinton, G. E. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 18(6): 592-606. 1996.
Using Generative Models for Handwritten Digit Recognition [link] link   Using Generative Models for Handwritten Digit Recognition [link] abstract   Using Generative Models for Handwritten Digit Recognition [ps] ps   Using Generative Models for Handwritten Digit Recognition [pdf] pdf   link   bibtex  
Varieties of Helmholtz Machine. Dayan, P.; and Hinton, G. E. Neural Networks (NN), 9(8): 1385-1403. 1996.
Varieties of Helmholtz Machine [link] link   Varieties of Helmholtz Machine [link] abstract   Varieties of Helmholtz Machine [pdf] pdf   link   bibtex   1 download  
The EM algorithm for mixtures of factor analyzers. Ghahramani, Z.; Hinton, G. E.; and others Technical Report Technical Report CRG-TR-96-1, University of Toronto, 1996.
The EM algorithm for mixtures of factor analyzers [link] abstract   The EM algorithm for mixtures of factor analyzers [link] psgz   The EM algorithm for mixtures of factor analyzers [pdf] pdf   link   bibtex  
Parameter estimation for linear dynamical systems. Ghahramani, Z.; and Hinton, G. E. Technical Report Technical Report CRG-TR-96-2, University of Totronto, Dept. of Computer Science, 1996.
Parameter estimation for linear dynamical systems [link] abstract   Parameter estimation for linear dynamical systems [link] psgz   Parameter estimation for linear dynamical systems [pdf] pdf   link   bibtex  
Using neural networks to monitor for rare failures. Hinton, G. E.; and Frey, B. J. In Mechanical Working and Steel Processing Conference Proceedings, pages 545–548, 1996. IRON AND STEEL SOCIETY OF AIME
Using neural networks to monitor for rare failures [pdf] pdf   link   bibtex   1 download  
  1995 (7)
GloveTalkII: An Adaptive Gesture-to-Formant Interface. Fels, S.; and Hinton, G. E. In Proceedings of Human Factors in Computing Systems (CHI), pages 456-463, 1995.
GloveTalkII: An Adaptive Gesture-to-Formant Interface [link] link   link   bibtex  
Using Pairs of Data-Points to Define Splits for Decision Trees. Hinton, G. E.; and Revow, M. In Proceedings of Neural Information Processing Systems (NIPS), pages 507-513, 1995.
Using Pairs of Data-Points to Define Splits for Decision Trees [link] link   Using Pairs of Data-Points to Define Splits for Decision Trees [link] abstract   Using Pairs of Data-Points to Define Splits for Decision Trees [ps] ps   Using Pairs of Data-Points to Define Splits for Decision Trees [pdf] pdf   link   bibtex  
Does the Wake-sleep Algorithm Produce Good Density Estimators?. Frey, B. J.; Hinton, G. E.; and Dayan, P. In Proceedings of Neural Information Processing Systems (NIPS), pages 661-667, 1995.
Does the Wake-sleep Algorithm Produce Good Density Estimators? [link] link   Does the Wake-sleep Algorithm Produce Good Density Estimators? [link] abstract   Does the Wake-sleep Algorithm Produce Good Density Estimators? [ps] ps   Does the Wake-sleep Algorithm Produce Good Density Estimators? [link] psgz   Does the Wake-sleep Algorithm Produce Good Density Estimators? [pdf] pdf   link   bibtex  
The Helmholtz machine. Dayan, P.; Hinton, G. E.; Neal, R. M.; and Zemel, R. S. Neural Computation (NECO), 7(5): 889-904. 1995.
The Helmholtz machine [link] link   The Helmholtz machine [link] abstract   The Helmholtz machine [link] psgz   The Helmholtz machine [pdf] pdf   link   bibtex   1 download  
Learning Population Codes by Minimizing Description Length. Zemel, R. S.; and Hinton, G. E. Neural Computation, 7: 549–564. 1995.
Learning Population Codes by Minimizing Description Length [link] abstract   Learning Population Codes by Minimizing Description Length [link] psgz   Learning Population Codes by Minimizing Description Length [pdf] pdf   link   bibtex  
The Wake-Sleep Algorithm for Unsupervised Neural Networks. Hinton, G. E.; Dayan, P.; Frey, B. J.; and Neal, R. M. 1995.
The Wake-Sleep Algorithm for Unsupervised Neural Networks [link] abstract   The Wake-Sleep Algorithm for Unsupervised Neural Networks [ps] ps   The Wake-Sleep Algorithm for Unsupervised Neural Networks [pdf] pdf   link   bibtex   2 downloads  
The Helmholtz machine through time. Hinton, G.; Dayan, P.; To, A.; and Neal, R. In International Conference on Artificial Neural Networks (ICANN-95), pages 483–490, 1995.
The Helmholtz machine through time [link] abstract   The Helmholtz machine through time [ps] ps   The Helmholtz machine through time [pdf] pdf   link   bibtex  
  1994 (5)
An Alternative Model for Mixtures of Experts. Xu, L.; Jordan, M. I.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 633-640, 1994.
An Alternative Model for Mixtures of Experts [link] link   An Alternative Model for Mixtures of Experts [pdf] pdf   link   bibtex  
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks. Fels, S.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 843-850, 1994.
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks [link] link   Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks [pdf] pdf   link   bibtex  
Using a neural net to instantiate a deformable model. Williams, C. K. I.; Revow, M.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 965-972, 1994.
Using a neural net to instantiate a deformable model [link] link   Using a neural net to instantiate a deformable model [link] abstract   Using a neural net to instantiate a deformable model [ps] ps   Using a neural net to instantiate a deformable model [pdf] pdf   link   bibtex  
Recognizing Handwritten Digits Using Mixtures of Linear Models. Hinton, G. E.; Revow, M.; and Dayan, P. In Proceedings of Neural Information Processing Systems (NIPS), pages 1015-1022, 1994.
Recognizing Handwritten Digits Using Mixtures of Linear Models [link] link   Recognizing Handwritten Digits Using Mixtures of Linear Models [link] abstract   Recognizing Handwritten Digits Using Mixtures of Linear Models [ps] ps   Recognizing Handwritten Digits Using Mixtures of Linear Models [link] psgz   Recognizing Handwritten Digits Using Mixtures of Linear Models [pdf] pdf   link   bibtex   1 download  
A modified gating network for the mixtures of experts architecture. Xu, L.; Jordan, M. I.; and Hinton, G. E. In Proceedings of: 1994 World Congress on Neural Networks, San Diego, CA, volume II, pages 405–410, 1994.
A modified gating network for the mixtures of experts architecture [pdf] pdf   link   bibtex  
  1993 (9)
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. Hinton, G. E.; and van Camp, D. In Proceedings of Computational Learning Theory (COLT), pages 5-13, 1993.
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights [link] link   Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights [link] abstract   Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights [ps] ps   Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights [pdf] pdf   link   bibtex   2 downloads  
A soft decision-directed LMS algorithm for blind equalization. Nowlan, S. J.; and Hinton, G. E. IEEE Transactions on Communications (TCOM), 41(2): 275-279. 1993.
A soft decision-directed LMS algorithm for blind equalization [link] link   A soft decision-directed LMS algorithm for blind equalization [pdf] pdf   link   bibtex  
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer. Fels, S. S.; and Hinton, G. E. IEEE Transactions on Neural Networks (TNN), 4(1): 2-8. 1993.
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer [link] link   Glove-Talk: a neural network interface between a data-glove and a speech synthesizer [link] abstract   Glove-Talk: a neural network interface between a data-glove and a speech synthesizer [ps] ps   Glove-Talk: a neural network interface between a data-glove and a speech synthesizer [pdf] pdf   link   bibtex  
Autoencoders, Minimum Description Length and Helmholtz Free Energy. Hinton, G. E.; and Zemel, R. S. In Proceedings of Neural Information Processing Systems (NIPS), pages 3-10, 1993.
Autoencoders, Minimum Description Length and Helmholtz Free Energy [link] link   Autoencoders, Minimum Description Length and Helmholtz Free Energy [link] abstract   Autoencoders, Minimum Description Length and Helmholtz Free Energy [ps] ps   Autoencoders, Minimum Description Length and Helmholtz Free Energy [pdf] pdf   link   bibtex   1 download  
Developing Population Codes by Minimizing Description Length. Zemel, R. S.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 11-18, 1993.
Developing Population Codes by Minimizing Description Length [link] link   Developing Population Codes by Minimizing Description Length [link] abstract   Developing Population Codes by Minimizing Description Length [ps] ps   Developing Population Codes by Minimizing Description Length [pdf] pdf   link   bibtex  
Learning Mixture Models of Spatial Coherence. Becker, S.; and Hinton, G. E. Neural Computation (NECO), 5(2): 267-277. 1993.
Learning Mixture Models of Spatial Coherence [link] link   Learning Mixture Models of Spatial Coherence [link] abstract   Learning Mixture Models of Spatial Coherence [pdf] pdf   link   bibtex  
Simulating brain damage. Hinton, G. E.; Plaut, D. C.; and Shallice, T. Scientific American, 269(4): 76–82. October 1993.
Simulating brain damage [link] link0   Simulating brain damage [link] link1   Simulating brain damage [link] link2   Simulating brain damage [link] link   Simulating brain damage [pdf] pdf   link   bibtex   abstract  
Hand-printed digit recognition using deformable models. Williams, C. K.; and Hinton, G. E. In Proceedings of the 1991 York Conference on Spatial Vision in Humans and Robots, pages 127, 1993. Cambridge University Press
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Using Mixtures of Deformable Models to Capture Variations in Hand Printed Digits. Revow, M.; Williams, C. K. I.; and Hinton, G. E. In Third International Workshop on Frontiers of Handwriting Recognition, 1993.
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  1992 (5)
Feudal Reinforcement Learning. Dayan, P.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 271-278, 1992.
Feudal Reinforcement Learning [link] link   Feudal Reinforcement Learning [link] abstract   Feudal Reinforcement Learning [link] psgz   Feudal Reinforcement Learning [pdf] pdf   link   bibtex  
Simplifying Neural Networks by Soft Weight-Sharing. Nowlan, S. J.; and Hinton, G. E. Neural Computation (NECO), 4(4): 473-493. 1992.
Simplifying Neural Networks by Soft Weight-Sharing [link] link   Simplifying Neural Networks by Soft Weight-Sharing [pdf] pdf   link   bibtex   1 download  
A self-organizing neural network that discovers surfaces in random-dot stereograms. Becker, S.; and Hinton, G. E. Nature, 355: 161–163. 1992. Commentary by Graeme Mitchison and Richard Durbin in the News and Views section of Nature
A self-organizing neural network that discovers surfaces in random-dot stereograms. [link] abstract   A self-organizing neural network that discovers surfaces in random-dot stereograms. [pdf] pdf   link   bibtex  
Combining two methods of recognizing hand-printed digits. Hinton, G.; Williams, C.; and Revow, M. Art. Neural Systems, 2: 53–60. 1992.
Combining two methods of recognizing hand-printed digits [pdf] pdf   link   bibtex  
How neural networks learn from experience. Hinton, G. E. Scientific American, 267(3): 145–151. 1992.
How neural networks learn from experience [pdf] pdf   link   bibtex   3 downloads  
  1991 (7)
Learning to Make Coherent Predictions in Domains with Discontinuities. Becker, S.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 372-379, 1991.
Learning to Make Coherent Predictions in Domains with Discontinuities [link] link   link   bibtex  
Adaptive Elastic Models for Hand-Printed Character Recognition. Hinton, G. E.; Williams, C. K. I.; and Revow, M. In Proceedings of Neural Information Processing Systems (NIPS), pages 512-519, 1991.
Adaptive Elastic Models for Hand-Printed Character Recognition [link] link   Adaptive Elastic Models for Hand-Printed Character Recognition [link] abstract   Adaptive Elastic Models for Hand-Printed Character Recognition [link] psgz   Adaptive Elastic Models for Hand-Printed Character Recognition [pdf] pdf   link   bibtex  
Adaptive Soft Weight Tying using Gaussian Mixtures. Nowlan, S. J.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 993-1000, 1991.
Adaptive Soft Weight Tying using Gaussian Mixtures [link] link   link   bibtex  
Adaptive Mixtures of Local Experts. Jacobs, R. A.; Jordan, M. I.; Nowlan, S. J.; and Hinton, G. E. Neural Computation, 3: 79–87. 1991.
Adaptive Mixtures of Local Experts [link] abstract   Adaptive Mixtures of Local Experts [ps] ps   Adaptive Mixtures of Local Experts [pdf] pdf   link   bibtex  
Lesioning an Attractor Network: Investigations of Acquired Dyslexia. Hinton, G. E.; and Shallice, T. Psychological Review, 98: 74–95. 1991.
Lesioning an Attractor Network: Investigations of Acquired Dyslexia [pdf] pdf   link   bibtex   1 download  
Deterministic Boltzmann Learning in Networks with Asymmetric Connectivity. Galland, C. C.; and Hinton, G. E. In Touretzky, D. S.; Elman, J. L.; Sejnowski, T. J.; and Hinton, G. E., editor(s), Connectionist Models: Proceedings of the 1990 Summer School, pages 3-9, San Mateo, CA, 1991. Morgan Kaufmann
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Mean field networks that learn to discriminate temporally distorted strings. Williams, C. K.; and Hinton, G. E. In Connectionist models: Proceedings of the 1990 summer school, pages 18–22, 1991. San Mateo, CA: Morgan Kaufmann
Mean field networks that learn to discriminate temporally distorted strings [link] abstract   Mean field networks that learn to discriminate temporally distorted strings [ps] ps   Mean field networks that learn to discriminate temporally distorted strings [pdf] pdf   link   bibtex  
  1990 (8)
Connectionist Symbol Processing - Preface. Hinton, G. E. Artif. Intell. (AI), 46(1-2): 1-4. 1990.
Connectionist Symbol Processing - Preface [link] link   Connectionist Symbol Processing - Preface [ps] ps   Connectionist Symbol Processing - Preface [pdf] pdf   link   bibtex  
Mapping Part-Whole Hierarchies into Connectionist Networks. Hinton, G. E. Artif. Intell. (AI), 46(1-2): 47-75. 1990.
Mapping Part-Whole Hierarchies into Connectionist Networks [link] link   Mapping Part-Whole Hierarchies into Connectionist Networks [pdf] pdf   link   bibtex  
Building adaptive interfaces with neural networks: The glove-talk pilot study. Fels, S.; and Hinton, G. E. In Proceedings of Workshop on Interaction between Compilers and Computer Architectures (INTERACT), pages 683-688, 1990.
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Discovering Viewpoint-Invariant Relationships That Characterize Objects. Zemel, R. S.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 299-305, 1990.
Discovering Viewpoint-Invariant Relationships That Characterize Objects [link] link   Discovering Viewpoint-Invariant Relationships That Characterize Objects [pdf] pdf   link   bibtex  
Evaluation of Adaptive Mixtures of Competing Experts. Nowlan, S. J.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 774-780, 1990.
Evaluation of Adaptive Mixtures of Competing Experts [link] link   Evaluation of Adaptive Mixtures of Competing Experts [link] abstract   Evaluation of Adaptive Mixtures of Competing Experts [ps] ps   Evaluation of Adaptive Mixtures of Competing Experts [pdf] pdf   link   bibtex  
A time-delay neural network architecture for isolated word recognition. Lang, K. J.; Waibel, A.; and Hinton, G. E. Neural Networks (NN), 3(1): 23-43. 1990.
A time-delay neural network architecture for isolated word recognition [link] link   A time-delay neural network architecture for isolated word recognition [pdf] pdf   link   bibtex  
An Unsupervised Learning Procedure That Discovers Surfaces in Random-Dot Stereograms. Hinton, G. E.; and Becker, S. In Proc. IEEE/INNS International Joint Conference on Neural Networks, volume 1, pages 218–222, 1990. Hillsdale, NJ. Erlbaum
An Unsupervised Learning Procedure That Discovers Surfaces in Random-Dot Stereograms [pdf] pdf   link   bibtex  
The bootstrap Widrow-Hoff rule as a cluster-formation algorithm. Hinton, G. E.; and Nowlan, S. J. Neural Computation, 2(3): 355–362. 1990.
The bootstrap Widrow-Hoff rule as a cluster-formation algorithm [pdf] pdf   link   bibtex  
  1989 (6)
Connectionist Learning Procedures. Hinton, G. E. Artif. Intell. (AI), 40(1-3): 185-234. 1989.
Connectionist Learning Procedures [link] link   Connectionist Learning Procedures [pdf] pdf   link   bibtex  
Phoneme recognition using time-delay neural networks. Waibel, A. H.; Hanazawa, T.; Hinton, G. E.; Shikano, K.; and Lang, K. J. IEEE Trans. Acoustics, Speech, and Signal Processing (TSP), 37(3): 328-339. 1989.
Phoneme recognition using time-delay neural networks [link] link   Phoneme recognition using time-delay neural networks [pdf] pdf   link   bibtex  
Dimensionality Reduction and Prior Knowledge in E-Set Recognition. Lang, K. J.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 178-185, 1989.
Dimensionality Reduction and Prior Knowledge in E-Set Recognition [link] link   Dimensionality Reduction and Prior Knowledge in E-Set Recognition [pdf] pdf   link   bibtex  
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. Zemel, R. S.; Mozer, M.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 266-273, 1989.
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations [link] link   TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations [pdf] pdf   link   bibtex   1 download  
Discovering High Order Features with Mean Field Modules. Galland, C. C.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 509-515, 1989.
Discovering High Order Features with Mean Field Modules [link] link   Discovering High Order Features with Mean Field Modules [pdf] pdf   link   bibtex  
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space. Hinton, G. E. Neural Computation, 1: 143–150. 1989.
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space [pdf] pdf   link   bibtex   1 download  
  1988 (4)
A Distributed Connectionist Production System. Touretzky, D. S.; and Hinton, G. E. Cognitive Science (COGSCI), 12(3): 423-466. 1988.
A Distributed Connectionist Production System [link] link   A Distributed Connectionist Production System [pdf] pdf   link   bibtex   1 download  
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection. LeCun, Y.; Galland, C. C.; and Hinton, G. E. In Proceedings of Neural Information Processing Systems (NIPS), pages 141-148, 1988.
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection [link] link   GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection [pdf] pdf   link   bibtex  
Scene-Based and Viewer-Centered Representations for Comparing Shapes. Hinton, G. E.; and Parsons, L. M. Cognition, 30: 1–35. 1988.
Scene-Based and Viewer-Centered Representations for Comparing Shapes [pdf] pdf   link   bibtex  
Representing Part-Whole Hierarchies in Connectionist Networks. Hinton, G. E. In Proceedings of the 10th Annual Conference of the Cognitive Science Society, 1988. Hillsdale, NJ: Erlbaum
Representing Part-Whole Hierarchies in Connectionist Networks [pdf] pdf   link   bibtex  
  1987 (8)
Connectionist Architectures for Artificial Intelligence. Fahlman, S. E.; and Hinton, G. E. IEEE Computer (COMPUTER), 20(1): 100-109. 1987.
Connectionist Architectures for Artificial Intelligence [link] link   Connectionist Architectures for Artificial Intelligence [pdf] pdf   link   bibtex   3 downloads  
Learning Representations by Recirculation. Hinton, G. E.; and McClelland, J. L. In Proceedings of Neural Information Processing Systems (NIPS), pages 358-366, 1987.
Learning Representations by Recirculation [link] link   Learning Representations by Recirculation [pdf] pdf   link   bibtex  
Learning Translation Invariant Recognition in Massively Parallel Networks. Hinton, G. E. In Proceedings of Parallel Architectures and Languages Europe (PARLE), pages 1-13, 1987.
Learning Translation Invariant Recognition in Massively Parallel Networks [link] link   Learning Translation Invariant Recognition in Massively Parallel Networks [pdf] pdf   link   bibtex   2 downloads  
Using Fast Weights to Deblur Old Memories. Hinton, G. E.; and Plaut, D. C. In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pages 177–186, 1987. Hillsdale, NJ: Erlbaum
Using Fast Weights to Deblur Old Memories [pdf] pdf   link   bibtex  
How Learning Can Guide Evolution. Hinton, G. E.; and Nowlan, S. J. Complex Systems, 1: 495–502. 1987. Commentary by John Maynard Smith in the News and Views section of Nature.
How Learning Can Guide Evolution [pdf] pdf   link   bibtex   1 download  
Separating Figure from Ground with a Boltzmann Machine. Sejnowski, T. J.; and Hinton, G. E. In Arbib, M. A.; and Hanson, A. R., editor(s), Vision, Brain, and Cooperative Computation, 19, pages 703-723. MIT Press, Cambridge, MA, 1987.
Separating Figure from Ground with a Boltzmann Machine [pdf] pdf   link   bibtex  
The horizontal–vertical delusion. Hinton, G. E. Perception, 16(5): 677–680. 1987.
The horizontal–vertical delusion [pdf] pdf   link   bibtex  
Learning sets of filters using back-propagation. Plaut, D. C.; and Hinton, G. E. Computer Speech & Language, 2(1): 35–61. 1987.
Learning sets of filters using back-propagation [pdf] pdf   link   bibtex   1 download  
  1986 (13)
Learning in Massively Parallel Nets (Panel). McDermott, D. V.; and Hinton, G. E. In Proceedings of National Conference on Artificial Intelligence (AAAI), pages 1149, 1986.
Learning in Massively Parallel Nets (Panel) [link] link   link   bibtex  
Learning representations by back-propagating errors. Rumelhart, D. E.; Hinton, G. E.; and Williams, R. J. Nature, 323: 533–536. 1986. Commentary from News and Views section of Nature
Learning representations by back-propagating errors [pdf] pdf   link   bibtex   2 downloads  
Experiments on learning by back-propagation. Plaut, D. C.; Nowlan, S. J.; and Hinton, G. E. Technical Report CMU–CS–86–126, Carnegie–Mellon University, Pittsburgh, PA, 1986.
Experiments on learning by back-propagation [pdf] pdf   link   bibtex   1 download  
The Appeal of Parallel Distributed Processing. Mcclelland, J. L.; Rumelhart, D. E.; and Hinton, G. E. In Rumelhart, D. E.; and Mcclelland, J. L., editor(s), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, pages 3–44. MIT Press, Cambridge, MA, 1986.
The Appeal of Parallel Distributed Processing [pdf] pdf   link   bibtex  
A General Framework for Parallel Distributed Processing. Rumelhart, D. E.; Hinton, G. E.; and Mcclelland, J. L. In Rumelhart, D. E.; and Mcclelland, J. L., editor(s), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, pages 45–76. MIT Press, Cambridge, MA, 1986.
A General Framework for Parallel Distributed Processing [pdf] pdf   link   bibtex  
Distributed Representations. Hinton, G. E.; Mcclelland, J. L.; and Rumelhart, D. E. In Rumelhart, D. E.; and Mcclelland, J. L., editor(s), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, pages 77–109. MIT Press, Cambridge, MA, 1986.
Distributed Representations [pdf] pdf   link   bibtex   2 downloads  
Learning and Relearning in Boltzmann Machines. Hinton, G. E.; and Sejnowski, T. J. In Rumelhart, D. E.; and Mcclelland, J. L., editor(s), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, pages 282–317. MIT Press, Cambridge, MA, 1986.
Learning and Relearning in Boltzmann Machines [pdf] pdf   link   bibtex  
Learning Internal Representations by Error Propagation. Rumelhart, D. E.; Hinton, G. E.; and Williams, R. J. In Rumelhart, D. E.; and Mcclelland, J. L., editor(s), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, pages 318–362. MIT Press, Cambridge, MA, 1986.
Learning Internal Representations by Error Propagation [pdf] pdf   link   bibtex  
G-Maximization: An Unsupervised Learning Procedure for Discovering Regularities. Pearlmutter, B. A.; and Hinton, G. E. In Denker, J. S., editor(s), Neural Networks for Computing: American Institute of Physics Conference Proceedings 151, volume 2, pages 333–338, 1986.
G-Maximization: An Unsupervised Learning Procedure for Discovering Regularities [pdf] pdf   link   bibtex  
Separating figure from ground with a parallel network. Kienker, P. K.; Sejnowski, T. J.; Hinton, G. E.; and Schumacher, L. E. Perception, 15(2): 197–216. 1986.
Separating figure from ground with a parallel network [pdf] pdf   link   bibtex   1 download  
Learning symmetry groups with hidden units: Beyond the perceptron. Sejnowski, T. J.; Kienker, P. K.; and Hinton, G. E. Physica,260–275. 1986.
Learning symmetry groups with hidden units: Beyond the perceptron [pdf] pdf   link   bibtex  
Learning distributed representations of concepts. Hinton, G. E. In Proceedings of the eighth annual conference of the cognitive science society, pages 1–12, 1986. Amherst, MA
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Parallel distributed models of schemata and sequential thought processes. Rumelhart, D. E.; Smolensky, P.; McClelland, J. L.; and Hinton, G. E. In McClelland, J. L.; and Rumelhart, D. E., editor(s), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 2: Psychological and Biological Models, pages 7–57. MIT Press, Cambridge, MA, 1986.
Parallel distributed models of schemata and sequential thought processes [pdf] pdf   link   bibtex  
  1985 (5)
A Learning Algorithm for Boltzmann Machines. Ackley, D. H.; Hinton, G. E.; and Sejnowski, T. J. Cognitive Science (COGSCI), 9(1): 147-169. 1985.
A Learning Algorithm for Boltzmann Machines [link] link   A Learning Algorithm for Boltzmann Machines [pdf] pdf   link   bibtex   2 downloads  
Symbols Among the Neurons: Details of a Connectionist Inference Architecture. Touretzky, D. S.; and Hinton, G. E. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 238-243, 1985.
Symbols Among the Neurons: Details of a Connectionist Inference Architecture [pdf] pdf   link   bibtex  
Shape Recognition and Illusory Conjunctions. Hinton, G. E.; and Lang, K. J. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 252-259, 1985.
Shape Recognition and Illusory Conjunctions [pdf] pdf   link   bibtex  
Learning in parallel networks. Hinton, G. E. Byte, 10(4): 265–273. 1985.
Learning in parallel networks [pdf] pdf   link   bibtex   1 download  
Solving random-dot stereograms using the heat equation. Szeliski, R.; and Hinton, G. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’85), pages 284–288, 1985.
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  1984 (5)
Boltzmann Machines: Constraint Satisfaction Networks That Learn. Hinton, G. E.; Sejnowski, T. J.; and Ackley, D. H. Technical Report CMU-CS-84-119, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 1984.
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Parallel computations for controlling an arm. Hinton, G. Journal of motor behavior, 16(2): 171–194. 1984.
Parallel computations for controlling an arm [pdf] pdf   link   bibtex  
Why the islands move. Hutchins, E.; and Hinton, G. E. Perception, 13(5): 629–632. 1984.
Why the islands move [pdf] pdf   link   bibtex  
Evaluating the interface of a document processor: A comparison of expert judgment and user observation. Hammond, N.; Hinton, G.; Barnard, P.; MacLean, A.; Long, J.; and Whitefield, A. In Human-Computer Interaction-INTERACT, volume 84, 1984.
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Some computational solutions to Bernstein’s problems. Hinton, G. E. Human Motor Actions—Bernstein Reassessed,413–438. 1984.
Some computational solutions to Bernstein’s problems [pdf] pdf   link   bibtex  
  1983 (4)
Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines. Fahlman, S. E.; Hinton, G. E.; and Sejnowski, T. J. In Proceedings of National Conference on Artificial Intelligence (AAAI), pages 109-113, 1983.
Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines [link] link   Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines [pdf] pdf   link   bibtex  
Optimal Perceptual Inference. Hinton, G. E.; and Sejnowski, T. J. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1983.
Optimal Perceptual Inference [pdf] pdf   link   bibtex  
Parallel visual computation. Ballard, D. H.; Hinton, G. E.; and Sejnowski, T. J. Nature, 306(5938): 21–26. 1983.
Parallel visual computation [pdf] pdf   link   bibtex  
Analyzing cooperative computation. Hinton, G. E.; and Sejnowski, T. J. In Proceedings of the Fifth Annual Conference of the Cognitive Science Society, Rochester NY, 1983.
Analyzing cooperative computation [pdf] pdf   link   bibtex   3 downloads  
  1981 (7)
A Parallel Computation that Assigns Canonical Object-Based Frames of Reference. Hinton, G. E. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 683-685, 1981.
A Parallel Computation that Assigns Canonical Object-Based Frames of Reference [pdf] pdf   link   bibtex  
Shape Representation in Parallel Systems. Hinton, G. E. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 1088-1096, 1981.
Shape Representation in Parallel Systems [pdf] pdf   link   bibtex  
Implementing Semantic Networks in Parallel Hardware. Hinton, G. E. In Hinton, G. E.; and Anderson, J. A., editor(s), Parallel Models of Associative Memory, pages 161–187. Erlbaum, Hillsdale, NJ, 1981.
Implementing Semantic Networks in Parallel Hardware [pdf] pdf   link   bibtex   3 downloads  
The role of spatial working memory in shape perception. Hinton, G. E. In Third Annual Conference of the Cognitive Science Society, Berkeley, 1981.
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Parallel models of associative memory. Anderson, J. A.; and Hinton, G. E. Lawrence Erlbaum Associates, Hillsdale, NJ, 1981.
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Models of information processing in the brain. Anderson, J. A.; and Hinton, G. E. Parallel models of associative memory,9–48. 1981.
Models of information processing in the brain [pdf] pdf   link   bibtex   2 downloads  
Frames of reference and mental imagery. Hinton, G. E.; and Parsons, L. M. Attention and performance IX,261–277. 1981.
Frames of reference and mental imagery [pdf] pdf   link   bibtex   1 download  
  1979 (2)
Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery. Hinton, G. E. Cognitive Science (COGSCI), 3(3): 231-250. 1979.
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Imagery without arrays. Hinton, G. E. Behavioral and Brain Sciences, 2(04): 555–556. 1979.
Imagery without arrays [pdf] pdf   link   bibtex  
  1978 (2)
Representation and Control in Vision. Sloman, A.; Owen, D.; Hinton, G. E.; Birch, F.; and O'Gorman, F. In Proceedings of AISB/GI (ECAI), pages 309-314, 1978.
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Respectively Reconsidered'. Hinton, G. E. Pragmatics Microfiche, 3: 912–914. 1978.
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  1977 (1)
Relaxation and its role in vision. Hinton, G. E. . 1977.
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  1976 (1)
Using Relaxation to find a Puppet. Hinton, G. E. In Proceedings of AISB (ECAI), pages 148-157, 1976.
Using Relaxation to find a Puppet [pdf] pdf   link   bibtex   4 downloads