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Geoffrey E. Hinton - Publications

Affiliations: 
Computer Science University of Toronto, Toronto, ON, Canada 
Area:
machine learning
Website:
http://www.cs.toronto.edu/~hinton/
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122 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
2023 Azizi S, Culp L, Freyberg J, Mustafa B, Baur S, Kornblith S, Chen T, Tomasev N, Mitrović J, Strachan P, Mahdavi SS, Wulczyn E, Babenko B, Walker M, Loh A, ... ... Hinton G, et al. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nature Biomedical Engineering. PMID 37291435 DOI: 10.1038/s41551-023-01049-7  0.745
2020 Lillicrap TP, Santoro A, Marris L, Akerman CJ, Hinton G. Backpropagation and the brain. Nature Reviews. Neuroscience. PMID 32303713 DOI: 10.1038/S41583-020-0277-3  0.353
2015 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 521: 436-44. PMID 26017442 DOI: 10.1038/Nature14539  0.661
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  0.808
2014 Hinton G. Where do features come from? Cognitive Science. 38: 1078-1101. PMID 23800216 DOI: 10.1111/cogs.12049  0.46
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  0.47
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.  0.61
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  0.781
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  0.801
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  0.557
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  0.668
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  0.507
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  0.452
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.  0.625
2013 Tang Y, Salakhutdinov R, Hinton G. Tensor analyzers 30th International Conference On Machine Learning, Icml 2013. 1200-1208.  0.553
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  0.688
2012 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks Advances in Neural Information Processing Systems. 2: 1097-1105. DOI: 10.1145/3065386  0.789
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  0.398
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  0.671
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  0.803
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  0.576
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  0.602
2012 Salakhutdinov R, Hinton G. A better way to pretrain Deep Boltzmann Machines Advances in Neural Information Processing Systems. 3: 2447-2455.  0.589
2012 Tang Y, Salakhutdinov R, Hinton G. Deep Lambertian networks Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1623-1630.  0.601
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.  0.555
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  0.687
2011 Hinton GE. Machine learning for neuroscience. Neural Systems & Circuits. 1: 12. PMID 22330889 DOI: 10.1186/2042-1001-1-12  0.417
2011 Hinton GE. Technical perspective a better way to learn features Communications of the Acm. 54: 94. DOI: 10.1145/2001269.2001294  0.354
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  0.567
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  0.762
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  0.725
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.  0.767
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  0.748
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  0.808
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  0.393
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  0.78
2010 Susskind J, Anderson A, Hinton G. Turn that frown upside-down! Inferring facial actions from pairs of images in a neurally plausible computational model Journal of Vision. 10: 666-666. DOI: 10.1167/10.7.666  0.782
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  0.533
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  0.523
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  0.7
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  0.769
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.  0.68
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.  0.712
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.  0.688
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  0.747
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  0.534
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  0.768
2009 Salakhutdinov R, Hinton G. Semantic hashing International Journal of Approximate Reasoning. 50: 969-978. DOI: 10.1016/j.ijar.2008.11.006  0.663
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.  0.73
2009 Salakhutdinov R, Hinton G. Deep Boltzmann machines Journal of Machine Learning Research. 5: 448-455.  0.582
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.  0.612
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.  0.636
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  0.794
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  0.732
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  0.374
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  0.45
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  0.565
2007 Taylor GW, Hinton GE, Roweis S. Modeling human motion using binary latent variables Advances in Neural Information Processing Systems. 1345-1352.  0.77
2007 Salakhutdinov R, Hinton G. Learning a nonlinear embedding by preserving class neighbourhood structure Journal of Machine Learning Research. 2: 412-419.  0.615
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  0.792
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  0.67
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  0.801
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  0.809
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  0.763
2005 Goldberger J, Roweis S, Hinton G, Salakhutdinov R. Neighbourhood components analysis Advances in Neural Information Processing Systems.  0.748
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  0.787
2004 Teh YW, Welling M, Osindero S, Hinton GE. Energy-based models for sparse overcomplete representations Journal of Machine Learning Research. 4: 1235-1260. DOI: 10.1162/Jmlr.2003.4.7-8.1235  0.804
2003 Hinton G. The ups and downs of Hebb synapses. Canadian Psychology/Psychologie Canadienne. 44: 10-13. DOI: 10.1037/H0085812  0.413
2003 Welling M, Zemel RS, Hinton GE. Self supervised boosting Advances in Neural Information Processing Systems.  0.682
2002 Hinton GE. Training products of experts by minimizing contrastive divergence. Neural Computation. 14: 1771-800. PMID 12180402 DOI: 10.1162/089976602760128018  0.383
2002 Friston KJ, Penny W, Phillips C, Kiebel S, Hinton G, Ashburner J. Classical and Bayesian inference in neuroimaging: theory. Neuroimage. 16: 465-83. PMID 12030832 DOI: 10.1006/Nimg.2002.1090  0.459
2002 Oore S, Terzopoulos D, Hinton G. Local physical models for interactive character animation Computer Graphics Forum. 21: 337-346. DOI: 10.1111/1467-8659.00593  0.771
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  0.465
2002 Roweis S, Saul LK, Hinton GE. Global coordination of local linear models Advances in Neural Information Processing Systems.  0.757
2002 Paccanaro A, Hinton GE. Learning hierarchical structures with linear relational embedding Advances in Neural Information Processing Systems.  0.66
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  0.698
2001 Teh YW, Hinton GE. Rate-coded restricted boltzmann machines for face recognition Advances in Neural Information Processing Systems.  0.538
2000 Hinton GE. Computation by neural networks. Nature Neuroscience. 3: 1170. PMID 11127833 DOI: 10.1038/81442  0.303
2000 Ueda N, Nakano R, Ghahramani Z, Hinton GE. SMEM algorithm for mixture models. Neural Computation. 12: 2109-28. PMID 10976141  0.661
2000 Ghahramani Z, Hinton GE. Variational learning for switching state-space models. Neural Computation. 12: 831-64. PMID 10770834 DOI: 10.1162/089976600300015619  0.722
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.  0.6
2000 Hinton GE, Ghahramani Z, Teh YW. Learning to parse images Advances in Neural Information Processing Systems. 463-469.  0.756
2000 Paccanaro A, Hinton GE. Extracting distributed representations of concepts and relations from positive and negative propositions Proceedings of the International Joint Conference On Neural Networks. 2: 259-264.  0.609
1999 Frey BJ, Hinton GE. Variational learning in nonlinear gaussian belief networks. Neural Computation. 11: 193-213. PMID 9950729 DOI: 10.1162/089976699300016872  0.655
1999 Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R. A comparison of statistical learning methods on the Gusto database. Statistics in Medicine. 17: 2501-8. PMID 9819841 DOI: 10.1002/(Sici)1097-0258(19981115)17:21<2501::Aid-Sim938>3.0.Co;2-M  0.353
1998 de Sa VR, Hinton GE. Cascaded redundancy reduction. Network (Bristol, England). 9: 73-84. PMID 9861979 DOI: 10.1088/0954-898X/9/1/004  0.733
1998 Ghahramani Z, Hinton GE. Hierarchical non-linear factor analysis and topographic maps Advances in Neural Information Processing Systems. 486-492.  0.595
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  0.539
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  0.739
1997 Oore S, Hinton GE, Dudek G. A mobile robot that learns its place Neural Computation. 9: 683-699. DOI: 10.1162/Neco.1997.9.3.683  0.768
1997 Dayan P, Hinton GE. Using expectation-maximization for reinforcement learning Neural Computation. 9: 271-278. DOI: 10.1162/Neco.1997.9.2.271  0.496
1997 Frey BJ, Hinton GE. Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model Computer Journal. 40: x9-165. DOI: 10.1093/Comjnl/40.2_And_3.157  0.612
1997 Williams CKI, Revow M, Hinton GE. Instantiating Deformable Models with a Neural Net Computer Vision and Image Understanding. 68: 120-126. DOI: 10.1006/Cviu.1997.0540  0.398
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  0.53
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  0.411
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 DOI: 10.1126/Science.7761831  0.757
1995 Dayan P, Hinton GE, Neal RM, Zemel RS. The Helmholtz machine. Neural Computation. 7: 889-904. PMID 7584891 DOI: 10.1162/neco.1995.7.5.889  0.803
1995 Zemel RS, Hinton GE. Learning population codes by minimizing description length Neural Computation. 7: 549-564.  0.747
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  0.365
1993 Hinton GE, Plaut DC, Shallice T. Simulating brain damage. Scientific American. 269: 76-82. PMID 8235551 DOI: 10.1038/Scientificamerican1093-76  0.56
1993 Becker S, Hinton GE. Learning Mixture Models of Spatial Coherence Neural Computation. 5: 267-277. DOI: 10.1162/neco.1993.5.2.267  0.696
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  0.705
1992 Hinton GE. How neural networks learn from experience. Scientific American. 267: 144-51. PMID 1502516 DOI: 10.1038/Scientificamerican0992-144  0.369
1992 Nowlan SJ, Hinton GE. Simplifying Neural Networks by Soft Weight-Sharing Neural Computation. 4: 473-493. DOI: 10.1162/neco.1992.4.4.473  0.334
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  0.57
1991 Hinton GE, Shallice T. Lesioning an attractor network: investigations of acquired dyslexia. Psychological Review. 98: 74-95. PMID 2006233 DOI: 10.1037/0033-295X.98.1.74  0.304
1991 Becker S, Hinton GE. Learning spatially coherent properties of the visual world in connectionist networks Proceedings of Spie. 1569: 218-226. DOI: 10.1117/12.48380  0.71
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  0.361
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  0.434
1989 Hinton GE. Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space Neural Computation. 1: 143-150. DOI: 10.1162/neco.1989.1.1.143  0.385
1989 Waibel A, Hanazawa T, Hinton G, Shikano K, Lang KJ. Phoneme Recognition Using Time-Delay Neural Networks Ieee Transactions On Acoustics, Speech, and Signal Processing. 37: 328-339. DOI: 10.1109/29.21701  0.381
1989 Hinton GE. Connectionist learning procedures Artificial Intelligence. 40: 185-234. DOI: 10.1016/0004-3702(89)90049-0  0.387
1988 Waibel A, Hanazawa T, Shikano K, Hinton G, Lang K. Speech recognition using time‐delay neural networks Journal of the Acoustical Society of America. 83. DOI: 10.1121/1.2025362  0.388
1988 Touretzky DS, Hinton GE. A distributed connectionist production system Cognitive Science. 12: 423-466. DOI: 10.1016/0364-0213(88)90029-8  0.641
1987 Fahlman SE, Hinton GE. Connectionist Architectures for Artificial Intelligence Computer. 20: 100-109. DOI: 10.1109/MC.1987.1663364  0.708
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  0.655
1986 Kienker PK, Sejnowski TJ, Hinton GE, Schumacher LE. Separating figure from ground with a parallel network. Perception. 15: 197-216. PMID 3774489 DOI: 10.1068/P150197  0.523
1986 Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors Nature. 323: 533-536. DOI: 10.1038/323533a0  0.385
1986 Sejnowski TJ, Kienker PK, Hinton GE. Learning symmetry groups with hidden units: beyond the perceptron Physica D: Nonlinear Phenomena. 2: 260-275. DOI: 10.1016/0167-2789(86)90245-9  0.56
1985 Ackley DH, Hinton GE, Sejnowski TJ. A Learning Algorithm for Boltzmann Machines* Cognitive Science. 9: 147-169. DOI: 10.1207/S15516709Cog0901_7  0.79
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  0.737
1983 Ballard DH, Hinton GE, Sejnowski TJ. Parallel visual computation. Nature. 306: 21-6. PMID 6633656 DOI: 10.1038/306021a0  0.601
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