generated by bibbase.org
  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.
link   bibtex  
  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.
link   bibtex  
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