Geoffrey E. Hinton - Publications

Affiliations: 
Computer Science University of Toronto, Toronto, ON, Canada 
Area:
machine learning
Website:
http://www.cs.toronto.edu/~hinton/

163 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2015 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 521: 436-44. PMID 26017442 DOI: 10.1038/nature14539  1
2015 Ranzato M’, Hinton G, LeCun Y. Guest Editorial: Deep Learning International Journal of Computer Vision. 113: 1-2. DOI: 10.1007/s11263-015-0813-1  1
2014 Hinton G. Where do features come from? Cognitive Science. 38: 1078-1101. PMID 23800216 DOI: 10.1111/cogs.12049  1
2014 Sarikaya R, Hinton GE, Deoras A. Application of deep belief networks for natural language understanding Ieee Transactions On Audio, Speech and Language Processing. 22: 778-784. DOI: 10.1109/TASLP.2014.2303296  1
2014 Jaitly N, Vanhoucke V, Hinton G. Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 1905-1909.  1
2014 Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting Journal of Machine Learning Research. 15: 1929-1958.  1
2013 Ranzato M, Mnih V, Susskind JM, Hinton GE. Modeling natural images using gated MRFs. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 2206-22. PMID 23868780 DOI: 10.1109/TPAMI.2013.29  1
2013 Ranzato M, Mnih V, Susskind JM, Hinton GE. Modeling Natural Images Using Gated MRFs. Ieee Transactions On Pattern Analysis and Machine Intelligence. PMID 23358281  1
2013 Dahl GE, Sainath TN, Hinton GE. Improving deep neural networks for LVCSR using rectified linear units and dropout Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 8609-8613. DOI: 10.1109/ICASSP.2013.6639346  1
2013 Deng L, Hinton G, Kingsbury B. New types of deep neural network learning for speech recognition and related applications: An overview Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 8599-8603. DOI: 10.1109/ICASSP.2013.6639344  1
2013 Graves A, Mohamed AR, Hinton G. Speech recognition with deep recurrent neural networks Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 6645-6649. DOI: 10.1109/ICASSP.2013.6638947  1
2013 Zeiler MD, Ranzato M, Monga R, Mao M, Yang K, Le QV, Nguyen P, Senior A, Vanhoucke V, Dean J, Hinton GE. On rectified linear units for speech processing Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 3517-3521. DOI: 10.1109/ICASSP.2013.6638312  1
2013 Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representations by Error Propagation Readings in Cognitive Science: a Perspective From Psychology and Artificial Intelligence. 399-421. DOI: 10.1016/B978-1-4832-1446-7.50035-2  1
2013 Rumelhart DE, Smolensky P, McClelland JL, Hinton GE. Schemata and Sequential Thought Processes in PDP Models Readings in Cognitive Science: a Perspective From Psychology and Artificial Intelligence. 224-249. DOI: 10.1016/B978-1-4832-1446-7.50020-0  1
2013 McClelland JL, Rumelhart DE, Hinton GE. The Appeal of Parallel Distributed Processing Readings in Cognitive Science: a Perspective From Psychology and Artificial Intelligence. 52-72. DOI: 10.1016/B978-1-4832-1446-7.50010-8  1
2013 Jaitly N, Hinton GE. Using an autoencoder with deformable templates to discover features for automated speech recognition Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 1737-1740.  1
2013 Srivastava N, Salakhutdinov R, Hinton G. Modeling documents with a Deep Boltzmann Machine Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, Uai 2013. 616-624.  1
2013 Tang Y, Salakhutdinov R, Hinton G. Tensor analyzers 30th International Conference On Machine Learning, Icml 2013. 1200-1208.  1
2013 Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning 30th International Conference On Machine Learning, Icml 2013. 2176-2184.  1
2012 Salakhutdinov R, Hinton G. An efficient learning procedure for deep Boltzmann machines. Neural Computation. 24: 1967-2006. PMID 22509963 DOI: 10.1162/NECO_a_00311  1
2012 Yu D, Hinton G, Morgan N, Chien JT, Sagayama S. Introduction to the special section on deep learning for speech and language processing Ieee Transactions On Audio, Speech and Language Processing. 20: 4-6. DOI: 10.1109/TASL.2011.2173371  1
2012 Mohamed AR, Dahl GE, Hinton G. Acoustic modeling using deep belief networks Ieee Transactions On Audio, Speech and Language Processing. 20: 14-22. DOI: 10.1109/TASL.2011.2109382  1
2012 Hinton G, Deng L, Yu D, Dahl G, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T, Kingsbury B. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups Ieee Signal Processing Magazine. 29: 82-97. DOI: 10.1109/MSP.2012.2205597  1
2012 Mohamed AR, Hinton G, Penn G. Understanding how deep belief networks perform acoustic modelling Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 4273-4276. DOI: 10.1109/ICASSP.2012.6288863  1
2012 Tang Y, Salakhutdinov R, Hinton G. Robust Boltzmann Machines for recognition and denoising Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2264-2271. DOI: 10.1109/CVPR.2012.6247936  1
2012 Van Der Maaten L, Hinton G. Visualizing non-metric similarities in multiple maps Machine Learning. 87: 33-55. DOI: 10.1007/s10994-011-5273-4  1
2012 Hinton GE. A practical guide to training restricted boltzmann machines Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7700: 599-619. DOI: 10.1007/978-3-642-35289-8-32  1
2012 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks Advances in Neural Information Processing Systems. 2: 1097-1105.  1
2012 Salakhutdinov R, Hinton G. A better way to pretrain Deep Boltzmann Machines Advances in Neural Information Processing Systems. 3: 2447-2455.  1
2012 Tang Y, Salakhutdinov R, Hinton G. Deep Lambertian networks Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1623-1630.  1
2012 Tang Y, Salakhutdinov R, Hinton G. Deep mixtures of factor analysers Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 505-512.  1
2012 Mnih V, Hinton G. Learning to label aerial images from noisy data Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 567-574.  1
2011 Hinton G, Salakhutdinov R. Discovering binary codes for documents by learning deep generative models. Topics in Cognitive Science. 3: 74-91. PMID 25164175 DOI: 10.1111/j.1756-8765.2010.01109.x  1
2011 Hinton GE. Technical perspective a better way to learn features Communications of the Acm. 54: 94. DOI: 10.1145/2001269.2001294  1
2011 Jaitly N, Hinton G. Learning a better representation of speech soundwaves using restricted boltzmann machines Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 5884-5887. DOI: 10.1109/ICASSP.2011.5947700  1
2011 Sarikaya R, Hinton GE, Ramabhadran B. Deep belief nets for natural language call-routing Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 5680-5683. DOI: 10.1109/ICASSP.2011.5947649  1
2011 Mohamed AR, Sainath TN, Dahl G, Ramabhadran B, Hinton GE, Picheny MA. Deep belief networks using discriminative features for phone recognition Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 5060-5063. DOI: 10.1109/ICASSP.2011.5947494  1
2011 Ranzato M, Susskind J, Mnih V, Hinton G. On deep generative models with applications to recognition Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2857-2864. DOI: 10.1109/CVPR.2011.5995710  1
2011 Susskind J, Hinton G, Memisevic R, Pollefeys M. Modeling the joint density of two images under a variety of transformations Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2793-2800. DOI: 10.1109/CVPR.2011.5995541  1
2011 Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6791: 44-51. DOI: 10.1007/978-3-642-21735-7_6  1
2011 Mnih V, Larochelle H, Hinton GE. Conditional Restricted Boltzmann Machines for structured output prediction Proceedings of the 27th Conference On Uncertainty in Artificial Intelligence, Uai 2011. 514-522.  1
2011 Taylor GW, Hinton GE, Roweis ST. Two distributed-state models for generating high-dimensional time series Journal of Machine Learning Research. 12: 1025-1068.  1
2011 Sutskever I, Martens J, Hinton G. Generating text with recurrent neural networks Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 1017-1024.  1
2010 Schmah T, Yourganov G, Zemel RS, Hinton GE, Small SL, Strother SC. Comparing classification methods for longitudinal fMRI studies. Neural Computation. 22: 2729-62. PMID 20804386 DOI: 10.1162/NECO_a_00024  1
2010 Memisevic R, Hinton GE. Learning to represent spatial transformations with factored higher-order Boltzmann machines. Neural Computation. 22: 1473-92. PMID 20141471 DOI: 10.1162/neco.2010.01-09-953  1
2010 Hinton GE. Learning to represent visual input. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 365: 177-84. PMID 20008395 DOI: 10.1098/rstb.2009.0200  1
2010 Sutskever I, Hinton G. Temporal-Kernel Recurrent Neural Networks Neural Networks. 23: 239-243. PMID 19932002 DOI: 10.1016/j.neunet.2009.10.009  1
2010 Mohamed AR, Hinton G. Phone recognition using restricted boltzmann machines Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 4354-4357. DOI: 10.1109/ICASSP.2010.5495651  1
2010 Taylor GW, Sigal L, Fleet DJ, Hinton GE. Dynamical binary latent variable models for 3D human pose tracking Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 631-638. DOI: 10.1109/CVPR.2010.5540157  1
2010 Ranzato M, Hinton GE. Modeling pixel means and covariances using factorized third-order Boltzmann machines Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2551-2558. DOI: 10.1109/CVPR.2010.5539962  1
2010 Mnih V, Hinton GE. Learning to detect roads in high-resolution aerial images Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6316: 210-223. DOI: 10.1007/978-3-642-15567-3_16  1
2010 Ranzato MA, Mnih V, Hinton GE. Generating more realistic images using gated MRF's Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 1
2010 Ranzato M, Krizhevsky A, Hinton GE. Factored 3-way restricted Boltzmann machines for modeling natural images Journal of Machine Learning Research. 9: 621-628.  1
2010 Krizhevsky A, Hinton GE. Using very deep autoencoders for content-based image retrieval Esann 2011 Proceedings, 19th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning. 489-494.  1
2010 Nair V, Hinton GE. Rectified linear units improve Restricted Boltzmann machines Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 807-814.  1
2010 Deng L, Seltzer M, Yu D, Acero A, Mohamed A, Hinton G. Binary coding of speech spectrograms using a deep auto-encoder Proceedings of the 11th Annual Conference of the International Speech Communication Association, Interspeech 2010. 1692-1695.  1
2010 Larochelle H, Hinton G. Learning to combine foveal glimpses with a third-order Boltzmann machine Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 1
2010 Dahl GE, Ranzato M, Mohamed AR, Hinton G. Phone recognition with the mean-covariance restricted Boltzmann machine Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 1
2010 Memisevic R, Zach C, Hinton G, Pollefeys M. Gated softmax classification Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010 1
2009 Heess N, Williams CKI, Hinton GE. Learning generative texture models with extended Fields-of-Experts British Machine Vision Conference, Bmvc 2009 - Proceedings. DOI: 10.5244/C.23.115  1
2009 Tieleman T, Hinton G. Using fast weights to improve persistent contrastive divergence Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1033-1040. DOI: 10.1145/1553374.1553506  1
2009 Taylor GW, Hinton GE. Factored conditional restricted Boltzmann machines for modeling motion style Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1025-1032. DOI: 10.1145/1553374.1553505  1
2009 Mnih A, Yuecheng Z, Hinton G. Improving a statistical language model through non-linear prediction Neurocomputing. 72: 1414-1418. DOI: 10.1016/j.neucom.2008.12.025  1
2009 Salakhutdinov R, Hinton G. Semantic hashing International Journal of Approximate Reasoning. 50: 969-978. DOI: 10.1016/j.ijar.2008.11.006  1
2009 Nair V, Hinton GE. 3D object recognition with Deep Belief Nets Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1339-1347.  1
2009 Zeiler MD, Taylor GW, Troje NF, Hinton GE. Modeling pigeon behaviour using a conditional restricted boltzmann machine Esann 2009 Proceedings, 17th European Symposium On Artificial Neural Networks - Advances in Computational Intelligence and Learning. 391-396.  1
2009 Taylor GW, Hinton GE. Products of Hidden Markov Models: It takes N>1 to Tango Proceedings of the 25th Conference On Uncertainty in Artificial Intelligence, Uai 2009. 522-529.  1
2009 Schmah T, Hinton GE, Zemel RS, Small SL, Strother S. Generative versus discriminative training of RBMs for classification of fMRI images Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1409-1416.  1
2009 Osindero S, Hinton G. Modeling image patches with a directed hierarchy of Markov random fields Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 1
2009 Salakhutdinov R, Hinton G. Deep Boltzmann machines Journal of Machine Learning Research. 5: 448-455.  1
2009 Salakhutdinov R, Hinton G. Replicated softmax: An undirected topic model Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1607-1614.  1
2009 Palatucci M, Pomerleau D, Hinton G, Mitchell TM. Zero-shot learning with semantic output codes Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1410-1418.  1
2009 Sutskever I, Hinton G. Using matrices to model symbolic relationships Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1593-1600.  1
2009 Salakhutdinov R, Hinton G. Using deep belief nets to learn covariance kernels for Gaussian processes Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference 1
2009 Nair V, Hinton G. Implicit mixtures of Restricted Boltzmann machines Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1145-1152.  1
2009 Mnih A, Hinton G. A scalable hierarchical distributed language model Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1081-1088.  1
2009 Sutskever I, Hinton G, Taylor G. The recurrent temporal restricted boltzmann machine Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1601-1608.  1
2008 Sutskever I, Hinton GE. Deep, narrow sigmoid belief networks are universal approximators. Neural Computation. 20: 2629-36. PMID 18533819 DOI: 10.1162/neco.2008.12-07-661  1
2008 Nair V, Susskind J, Hinton GE. Analysis-by-synthesis by learning to invert generative black boxes Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5163: 971-981. DOI: 10.1007/978-3-540-87536-9_99  1
2008 Yuecheng Z, Mnih A, Hinton G. Improving a statistical language model by modulating the effects of context words Esann 2008 Proceedings, 16th European Symposium On Artificial Neural Networks - Advances in Computational Intelligence and Learning. 493-498.  1
2008 Van Der Maaten L, Hinton G. Visualizing data using t-SNE Journal of Machine Learning Research. 9: 2579-2625.  1
2007 Hinton GE. To recognize shapes, first learn to generate images. Progress in Brain Research. 165: 535-47. PMID 17925269 DOI: 10.1016/S0079-6123(06)65034-6  1
2007 Hinton GE. Learning multiple layers of representation. Trends in Cognitive Sciences. 11: 428-34. PMID 17921042 DOI: 10.1016/j.tics.2007.09.004  1
2007 Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering Acm International Conference Proceeding Series. 227: 791-798. DOI: 10.1145/1273496.1273596  1
2007 Mnih A, Hinton G. Three new graphical models for statistical language modelling Acm International Conference Proceeding Series. 227: 641-648. DOI: 10.1145/1273496.1273577  1
2007 Memisevic R, Hinton G. Unsupervised learning of image transformations Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR.2007.383036  1
2007 Taylor GW, Hinton GE, Roweis S. Modeling human motion using binary latent variables Advances in Neural Information Processing Systems. 1345-1352.  1
2007 Salakhutdinov R, Hinton G. Learning a nonlinear embedding by preserving class neighbourhood structure Journal of Machine Learning Research. 2: 412-419.  1
2007 Sutskever I, Hinton G. Learning multilevel distributed representations for high-dimensional sequences Journal of Machine Learning Research. 2: 548-555.  1
2007 Cook J, Sutskever I, Mnih A, Hinton G. Visualizing similarity data with a mixture of maps Journal of Machine Learning Research. 2: 67-74.  1
2006 Hinton G, Osindero S, Welling M, Teh YW. Unsupervised discovery of nonlinear structure using contrastive backpropagation. Cognitive Science. 30: 725-31. PMID 21702832 DOI: 10.1207/s15516709cog0000_76  1
2006 Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science (New York, N.Y.). 313: 504-7. PMID 16873662 DOI: 10.1126/science.1127647  1
2006 Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Computation. 18: 1527-54. PMID 16764513 DOI: 10.1162/neco.2006.18.7.1527  1
2006 Osindero S, Welling M, Hinton GE. Topographic product models applied to natural scene statistics. Neural Computation. 18: 381-414. PMID 16378519 DOI: 10.1162/089976606775093936  1
2005 Memisevic R, Hinton G. Improving dimensionality reduction with spectral gradient descent Neural Networks. 18: 702-710. PMID 16112551 DOI: 10.1016/j.neunet.2005.06.034  1
2005 Memisevic R, Hinton G. Embedding via clustering: Using spectral information to guide dimensionality reduction Proceedings of the International Joint Conference On Neural Networks. 5: 3198-3203. DOI: 10.1109/IJCNN.2005.1556439  1
2005 Mnih A, Hinton G. Learning nonlinear constraints with contrastive backpropagation Proceedings of the International Joint Conference On Neural Networks. 2: 1302-1307. DOI: 10.1109/IJCNN.2005.1556042  1
2005 Carreira-Perpiñán MA, Hinton GE. On contrastive divergence learning Aistats 2005 - Proceedings of the 10th International Workshop On Artificial Intelligence and Statistics. 33-40.  1
2005 Hinton GE. What kind of a graphical model is the brain? Ijcai International Joint Conference On Artificial Intelligence. 1765-1775.  1
2005 Hinton GE, Osindero S, Bao K. Learning causally linked Markov Random Fields Aistats 2005 - Proceedings of the 10th International Workshop On Artificial Intelligence and Statistics. 128-135.  1
2005 Hinton G, Nair V. Inferring motor programs from images of handwritten digits Advances in Neural Information Processing Systems. 515-522.  1
2005 Goldberger J, Roweis S, Hinton G, Salakhutdinov R. Neighbourhood components analysis Advances in Neural Information Processing Systems 1
2004 Welling M, Zemel RS, Hinton GE. Probabilistic sequential independent components analysis. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 15: 838-49. PMID 15461077 DOI: 10.1109/TNN.2004.828765  1
2004 Bishop CM, Svensén M, Hinton GE. Distinguishing text from graphics in on-line handwritten ink Proceedings - International Workshop On Frontiers in Handwriting Recognition, Iwfhr. 142-147. DOI: 10.1109/IWFHR.2004.34  1
2004 Teh YW, Welling M, Osindero S, Hinton GE. Energy-based models for sparse overcomplete representations Journal of Machine Learning Research. 4: 1235-1260.  1
2004 Sallans B, Hinton GE. Reinforcement learning with factored states and actions Journal of Machine Learning Research. 5: 1063-1088.  1
2003 Welling M, Zemel RS, Hinton GE. Self supervised boosting Advances in Neural Information Processing Systems 1
2002 Hinton GE. Training products of experts by minimizing contrastive divergence. Neural Computation. 14: 1771-800. PMID 12180402 DOI: 10.1162/089976602760128018  1
2002 Mayraz G, Hinton GE. Recognizing handwritten digits using hierarchical products of experts Ieee Transactions On Pattern Analysis and Machine Intelligence. 24: 189-197. DOI: 10.1109/34.982899  1
2002 Roweis S, Saul LK, Hinton GE. Global coordination of local linear models Advances in Neural Information Processing Systems 1
2002 Paccanaro A, Hinton GE. Learning hierarchical structures with linear relational embedding Advances in Neural Information Processing Systems 1
2002 Welling M, Hinton GE. A new learning algorithm for Mean Field Boltzmann Machines Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2415: 351-357.  1
2002 Brown AD, Hinton GE. Relative density nets: A new way to combine backpropagation with HMM's Advances in Neural Information Processing Systems 1
2001 Paccanaro A, Hinton GE. Learning distributed representations of concepts using Linear Relational Embedding Ieee Transactions On Knowledge and Data Engineering. 13: 232-244. DOI: 10.1109/69.917563  1
2001 Mayraz G, Hinton GE. Recognizing hand-written digits using hierarchical products of experts Advances in Neural Information Processing Systems 1
2001 Saltans B, Hinton GE. Using free energies to represent q-values in a multiagent reinforcement learning task Advances in Neural Information Processing Systems 1
2001 Teh YW, Hinton GE. Rate-coded restricted boltzmann machines for face recognition Advances in Neural Information Processing Systems 1
2000 Hinton GE. Computation by neural networks. Nature Neuroscience. 3: 1170. PMID 11127833 DOI: 10.1038/81442  1
2000 Ueda N, Nakano R, Ghahramani Z, Hinton GE. SMEM algorithm for mixture models. Neural Computation. 12: 2109-28. PMID 10976141  1
2000 Ghahramani Z, Hinton GE. Variational learning for switching state-space models. Neural Computation. 12: 831-64. PMID 10770834  1
2000 Ueda N, Nakano R, Ghahramani Z, Hinton GE. Split and merge EM algorithm for improving Gaussian mixture density estimates Journal of Vlsi Signal Processing Systems For Signal, Image, and Video Technology. 26: 133-140.  1
2000 Hinton GE, Ghahramani Z, Teh YW. Learning to parse images Advances in Neural Information Processing Systems. 463-469.  1
2000 Hinton GE, Brown AD. Spiking Boltzmann machines Advances in Neural Information Processing Systems. 122-128.  1
1999 Frey BJ, Hinton GE. Variational learning in nonlinear gaussian belief networks. Neural Computation. 11: 193-213. PMID 9950729  1
1998 Fels SS, Hinton GE. Glove-TalkII--a neural-network interface which maps gestures to parallel formant speech synthesizer controls. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 9: 205-12. PMID 18252442 DOI: 10.1109/72.655042  1
1998 de Sa VR, Hinton GE. Cascaded redundancy reduction. Network (Bristol, England). 9: 73-84. PMID 9861979  1
1998 Ghahramani Z, Hinton GE. Hierarchical non-linear factor analysis and topographic maps Advances in Neural Information Processing Systems. 486-492.  1
1997 Fels SS, Hinton GE. Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 8: 977-84. PMID 18255700 DOI: 10.1109/72.623199  1
1997 Hinton GE, Dayan P, Revow M. Modeling the manifolds of images of handwritten digits. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 8: 65-74. PMID 18255611 DOI: 10.1109/72.554192  1
1997 Hinton GE, Ghahramani Z. Generative models for discovering sparse distributed representations. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 352: 1177-90. PMID 9304685 DOI: 10.1098/rstb.1997.0101  1
1997 Frey BJ, Hinton GE. Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model Computer Journal. 40: x9-165.  1
1997 Williams CKI, Revow M, Hinton GE. Instantiating Deformable Models with a Neural Net Computer Vision and Image Understanding. 68: 120-126.  1
1997 Oore S, Hinton GE, Dudek G. A mobile robot that learns its place Neural Computation. 9: 683-699.  1
1997 Dayan P, Hinton GE. Using expectation-maximization for reinforcement learning Neural Computation. 9: 271-278.  1
1996 Hinton GE, Dayan P. Varieties of Helmholtz Machine. Neural Networks : the Official Journal of the International Neural Network Society. 9: 1385-1403. PMID 12662541 DOI: 10.1016/S0893-6080(96)00009-3  1
1996 Revow M, Williams CKI, Hinton GE. Using generative models for handwritten digit recognition Ieee Transactions On Pattern Analysis and Machine Intelligence. 18: 592-606. DOI: 10.1109/34.506410  1
1995 Hinton GE, Dayan P, Frey BJ, Neal RM. The "wake-sleep" algorithm for unsupervised neural networks. Science (New York, N.Y.). 268: 1158-61. PMID 7761831  1
1995 Dayan P, Hinton GE, Neal RM, Zemel RS. The Helmholtz machine. Neural Computation. 7: 889-904. PMID 7584891  1
1995 Zemel RS, Hinton GE. Learning population codes by minimizing description length Neural Computation. 7: 549-564.  1
1993 Fels SS, Hinton GE. Glove-Talk: a neural network interface between a data-glove and a speech synthesizer. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 4: 2-8. PMID 18267698 DOI: 10.1109/72.182690  1
1993 Hinton GE, Plaut DC, Shallice T. Simulating brain damage. Scientific American. 269: 76-82. PMID 8235551  1
1993 Nowlan SJ, Hinton GE. A Soft DecisioNDirected LMS Algorithm for Blind Equalization Ieee Transactions On Communications. 41: 275-279. DOI: 10.1109/26.216497  1
1992 Becker S, Hinton GE. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature. 355: 161-3. PMID 1729650 DOI: 10.1038/355161a0  1
1992 Hinton GE. How neural networks learn from experience. Scientific American. 267: 144-51. PMID 1502516  1
1991 Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE. Adaptive Mixtures of Local Experts. Neural Computation. 3: 79-87. PMID 31141872 DOI: 10.1162/neco.1991.3.1.79  0.92
1991 Hinton GE, Shallice T. Lesioning an attractor network: investigations of acquired dyslexia. Psychological Review. 98: 74-95. PMID 2006233  1
1990 Lang KJ, Waibel AH, Hinton GE. A time-delay neural network architecture for isolated word recognition Neural Networks. 3: 23-43. DOI: 10.1016/0893-6080(90)90044-L  1
1990 Hinton GE. Mapping part-whole hierarchies into connectionist networks Artificial Intelligence. 46: 47-75. DOI: 10.1016/0004-3702(90)90004-J  1
1990 Hinton GE. Preface to the special issue on connectionist symbol processing Artificial Intelligence. 46: 1-4. DOI: 10.1016/0004-3702(90)90002-H  1
1989 Hinton GE. Connectionist learning procedures Artificial Intelligence. 40: 185-234. DOI: 10.1016/0004-3702(89)90049-0  1
1988 Hinton GE, Parsons LM. Scene-based and viewer-centered representations for comparing shapes. Cognition. 30: 1-35. PMID 3180702 DOI: 10.1016/0010-0277(88)90002-9  1
1988 Touretzky DS, Hinton GE. A distributed connectionist production system Cognitive Science. 12: 423-466. DOI: 10.1016/0364-0213(88)90029-8  1
1987 Hinton GE. The horizontal-vertical delusion. Perception. 16: 677-80. PMID 3451195  1
1987 Fahlman SE, Hinton GE. Connectionist Architectures for Artificial Intelligence Computer. 20: 100-109. DOI: 10.1109/MC.1987.1663364  1
1987 Plaut DC, Hinton GE. Learning sets of filters using back-propagation Computer Speech and Language. 2: 35-61. DOI: 10.1016/0885-2308(87)90026-X  1
1986 Kienker PK, Sejnowski TJ, Hinton GE, Schumacher LE. Separating figure from ground with a parallel network. Perception. 15: 197-216. PMID 3774489  1
1986 Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors Nature. 323: 533-536. DOI: 10.1038/323533a0  1
1986 Sejnowski TJ, Kienker PK, Hinton GE. Learning symmetry groups with hidden units: Beyond the perceptron Physica D: Nonlinear Phenomena. 22: 260-270.  1
1985 Hinton GE. Three frames suffice Behavioral and Brain Sciences. 8: 296-297. DOI: 10.1017/S0140525X0002077X  1
1985 Ackley DH, Hinton GE, Sejnowski TJ. A learning algorithm for boltzmann machines Cognitive Science. 9: 147-169. DOI: 10.1016/S0364-0213(85)80012-4  1
1984 Hutchins E, Hinton GE. Why the islands move. Perception. 13: 629-32. PMID 6535986  1
1983 Ballard DH, Hinton GE, Sejnowski TJ. Parallel visual computation. Nature. 306: 21-6. PMID 6633656 DOI: 10.1038/306021a0  1
1980 Hinton GE. Inferring the meaning of direct perception Behavioral and Brain Sciences. 3: 387-388. DOI: 10.1017/S0140525X00005549  1
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