| 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 |
0.748 |
|
| 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 |
0.662 |
|
| 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 |
0.809 |
|
| 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. |
0.611 |
|
| 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 |
0.558 |
|
| 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 |
0.671 |
|
| 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 |
0.508 |
|
| 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 |
0.453 |
|
| 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. |
0.626 |
|
| 2013 |
Tang Y, Salakhutdinov R, Hinton G. Tensor analyzers 30th International Conference On Machine Learning, Icml 2013. 1200-1208. |
0.554 |
|
| 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 |
0.689 |
|
| 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 |
0.672 |
|
| 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 |
0.603 |
|
| 2012 |
Salakhutdinov R, Hinton G. A better way to pretrain Deep Boltzmann Machines Advances in Neural Information Processing Systems. 3: 2447-2455. |
0.591 |
|
| 2012 |
Tang Y, Salakhutdinov R, Hinton G. Deep Lambertian networks Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1623-1630. |
0.602 |
|
| 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. |
0.557 |
|
| 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 |
0.688 |
|
| 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 |
0.568 |
|
| 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 |
0.534 |
|
| 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 |
0.528 |
|
| 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 |
0.703 |
|
| 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. |
0.683 |
|
| 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. |
0.715 |
|
| 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. |
0.691 |
|
| 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 |
0.539 |
|
| 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 |
0.664 |
|
| 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. |
0.583 |
|
| 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. |
0.613 |
|
| 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. |
0.637 |
|
| 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 |
0.566 |
|
| 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. |
0.617 |
|
| 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 |
0.671 |
|
| 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 |
0.46 |
|
| 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. |
0.539 |
|
| 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 |
0.656 |
|
| 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 |
0.541 |
|
| 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 |
0.498 |
|
| 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 |
0.613 |
|
| 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 |
0.531 |
|
| 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 |
0.758 |
|
| 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 |
0.563 |
|
| 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 |
0.571 |
|
| 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 |
0.657 |
|
| 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 |
0.524 |
|
| 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 |
0.561 |
|
| 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 |
0.602 |
|
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