Geoffrey E. Hinton

Department of Computer Science, University of Toronto

	2013
i7		Geoffrey E. Hinton, Yee Whye Teh: Discovering Multiple Constraints that are Frequently Approximately Satisfied. CoRR abs/1301.2278 (2013)
i6		Alex Graves, Abdel-rahman Mohamed, Geoffrey E. Hinton: Speech Recognition with Deep Recurrent Neural Networks. CoRR abs/1303.5778 (2013)
	2012
j56		Laurens van der Maaten, Geoffrey E. Hinton: Visualizing non-metric similarities in multiple maps. Machine Learning 87(1): 33-55 (2012)
j55		Ruslan Salakhutdinov, Geoffrey E. Hinton: An Efficient Learning Procedure for Deep Boltzmann Machines. Neural Computation 24(8): 1967-2006 (2012)
j54		Dong Yu, Geoffrey E. Hinton, Nelson Morgan, Jen-Tzung Chien, Shigeki Sagayama: Introduction to the Special Section on Deep Learning for Speech and Language Processing. IEEE Transactions on Audio, Speech & Language Processing 20(1): 4-6 (2012)
j53		Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton: Acoustic Modeling Using Deep Belief Networks. IEEE Transactions on Audio, Speech & Language Processing 20(1): 14-22 (2012)
c108		Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton: Robust Boltzmann Machines for recognition and denoising. CVPR 2012: 2264-2271
c107		Abdel-rahman Mohamed, Geoffrey E. Hinton, Gerald Penn: Understanding how Deep Belief Networks perform acoustic modelling. ICASSP 2012: 4273-4276
c106		Volodymyr Mnih, Geoffrey E. Hinton: Learning to Label Aerial Images from Noisy Data. ICML 2012
c105		Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton: Deep Mixtures of Factor Analysers. ICML 2012
c104		Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton: Deep Lambertian Networks. ICML 2012
c103		Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton: ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012: 1106-1114
c102		Ruslan Salakhutdinov, Geoffrey E. Hinton: A Better Way to Pretrain Deep Boltzmann Machines. NIPS 2012: 2456-2464
i5		Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton: Conditional Restricted Boltzmann Machines for Structured Output Prediction. CoRR abs/1202.3748 (2012)
i4		Graham W. Taylor, Geoffrey E. Hinton: Products of Hidden Markov Models: It Takes N>1 to Tango. CoRR abs/1205.2614 (2012)
i3		Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton: Deep Mixtures of Factor Analysers. CoRR abs/1206.4635 (2012)
i2		Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov: Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs/1207.0580 (2012)
i1		Max Welling, Richard S. Zemel, Geoffrey E. Hinton: Efficient Parametric Projection Pursuit Density Estimation. CoRR abs/1212.2513 (2012)
	2011
j52		Geoffrey E. Hinton: A better way to learn features: technical perspective. Commun. ACM 54(10): 94 (2011)
j51		Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis: Two Distributed-State Models For Generating High-Dimensional Time Series. Journal of Machine Learning Research 12: 1025-1068 (2011)
c101		Joshua Susskind, Geoffrey E. Hinton, Roland Memisevic, Marc Pollefeys: Modeling the joint density of two images under a variety of transformations. CVPR 2011: 2793-2800
c100		Marc'Aurelio Ranzato, Joshua Susskind, Volodymyr Mnih, Geoffrey E. Hinton: On deep generative models with applications to recognition. CVPR 2011: 2857-2864
c99		Alex Krizhevsky, Geoffrey E. Hinton: Using very deep autoencoders for content-based image retrieval. ESANN 2011
c98		Geoffrey E. Hinton, Alex Krizhevsky, Sida D. Wang: Transforming Auto-Encoders. ICANN (1) 2011: 44-51
c97		Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, Michael A. Picheny: Deep Belief Networks using discriminative features for phone recognition. ICASSP 2011: 5060-5063
c96		Ruhi Sarikaya, Geoffrey E. Hinton, Bhuvana Ramabhadran: Deep belief nets for natural language call-routing. ICASSP 2011: 5680-5683
c95		Navdeep Jaitly, Geoffrey E. Hinton: Learning a better representation of speech soundwaves using restricted boltzmann machines. ICASSP 2011: 5884-5887
c94		Ilya Sutskever, James Martens, Geoffrey E. Hinton: Generating Text with Recurrent Neural Networks. ICML 2011: 1017-1024
c93		Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton: Conditional Restricted Boltzmann Machines for Structured Output Prediction. UAI 2011: 514-522
	2010
j50		Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton: Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images. Journal of Machine Learning Research - Proceedings Track 9: 621-628 (2010)
j49		Roland Memisevic, Geoffrey E. Hinton: Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines. Neural Computation 22(6): 1473-1492 (2010)
j48		Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Strother: Comparing Classification Methods for Longitudinal fMRI Studies. Neural Computation 22(11): 2729-2762 (2010)
j47		Ilya Sutskever, Geoffrey E. Hinton: Temporal-Kernel Recurrent Neural Networks. Neural Networks 23(2): 239-243 (2010)
c92		Graham W. Taylor, Leonid Sigal, David J. Fleet, Geoffrey E. Hinton: Dynamical binary latent variable models for 3D human pose tracking. CVPR 2010: 631-638
c91		Marc'Aurelio Ranzato, Geoffrey E. Hinton: Modeling pixel means and covariances using factorized third-order boltzmann machines. CVPR 2010: 2551-2558
c90		Volodymyr Mnih, Geoffrey E. Hinton: Learning to Detect Roads in High-Resolution Aerial Images. ECCV (6) 2010: 210-223
c89		Abdel-rahman Mohamed, Geoffrey E. Hinton: Phone recognition using Restricted Boltzmann Machines. ICASSP 2010: 4354-4357
c88		Vinod Nair, Geoffrey E. Hinton: Rectified Linear Units Improve Restricted Boltzmann Machines. ICML 2010: 807-814
c87		Li Deng, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, Geoffrey E. Hinton: Binary coding of speech spectrograms using a deep auto-encoder. INTERSPEECH 2010: 1692-1695
c86		George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton: Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS 2010: 469-477
c85		Hugo Larochelle, Geoffrey E. Hinton: Learning to combine foveal glimpses with a third-order Boltzmann machine. NIPS 2010: 1243-1251
c84		Roland Memisevic, Christopher Zach, Geoffrey E. Hinton, Marc Pollefeys: Gated Softmax Classification. NIPS 2010: 1603-1611
c83		Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton: Generating more realistic images using gated MRF's. NIPS 2010: 2002-2010
r2		Geoffrey E. Hinton: Boltzmann Machines. Encyclopedia of Machine Learning 2010: 132-136
r1		Geoffrey E. Hinton: Deep Belief Nets. Encyclopedia of Machine Learning 2010: 267-269
	2009
j46		Ruslan Salakhutdinov, Geoffrey E. Hinton: Semantic hashing. Int. J. Approx. Reasoning 50(7): 969-978 (2009)
j45		Andriy Mnih, Zhang Yuecheng, Geoffrey E. Hinton: Improving a statistical language model through non-linear prediction. Neurocomputing 72(7-9): 1414-1418 (2009)
j44		Ruslan Salakhutdinov, Geoffrey E. Hinton: Deep Boltzmann Machines. Journal of Machine Learning Research - Proceedings Track 5: 448-455 (2009)
j43		Geoffrey E. Hinton: Deep belief networks. Scholarpedia 4(5): 5947 (2009)
c82		Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton: Learning Generative Texture Models with extended Fields-of-Experts. BMVC 2009: 1-11
c81		Matthew D. Zeiler, Graham W. Taylor, Nikolaus F. Troje, Geoffrey E. Hinton: Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine. ESANN 2009
c80		Graham W. Taylor, Geoffrey E. Hinton: Factored conditional restricted Boltzmann Machines for modeling motion style. ICML 2009: 129
c79		Tijmen Tieleman, Geoffrey E. Hinton: Using fast weights to improve persistent contrastive divergence. ICML 2009: 130
c78		Kay Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio: Workshop summary: Workshop on learning feature hierarchies. ICML 2009: 165
c77		Vinod Nair, Geoffrey E. Hinton: 3D Object Recognition with Deep Belief Nets. NIPS 2009: 1339-1347
c76		Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell: Zero-shot Learning with Semantic Output Codes. NIPS 2009: 1410-1418
c75		Ruslan Salakhutdinov, Geoffrey E. Hinton: Replicated Softmax: an Undirected Topic Model. NIPS 2009: 1607-1614
c74		Graham W. Taylor, Geoffrey E. Hinton: Products of Hidden Markov Models: It Takes N>1 to Tango. UAI 2009: 522-529
	2008
j42		Ilya Sutskever, Geoffrey E. Hinton: Deep, Narrow Sigmoid Belief Networks Are Universal Approximators. Neural Computation 20(11): 2629-2636 (2008)
c73		Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton: Improving a statistical language model by modulating the effects of context words. ESANN 2008: 493-498
c72		Vinod Nair, Joshua Susskind, Geoffrey E. Hinton: Analysis-by-Synthesis by Learning to Invert Generative Black Boxes. ICANN (1) 2008: 971-981
c71		Andriy Mnih, Geoffrey E. Hinton: A Scalable Hierarchical Distributed Language Model. NIPS 2008: 1081-1088
c70		Vinod Nair, Geoffrey E. Hinton: Implicit Mixtures of Restricted Boltzmann Machines. NIPS 2008: 1145-1152
c69		Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother: Generative versus discriminative training of RBMs for classification of fMRI images. NIPS 2008: 1409-1416
c68		Ilya Sutskever, Geoffrey E. Hinton: Using matrices to model symbolic relationship. NIPS 2008: 1593-1600
c67		Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor: The Recurrent Temporal Restricted Boltzmann Machine. NIPS 2008: 1601-1608
	2007
j41		James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton: Visualizing Similarity Data with a Mixture of Maps. Journal of Machine Learning Research - Proceedings Track 2: 67-74 (2007)
j40		Ruslan Salakhutdinov, Geoffrey E. Hinton: Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. Journal of Machine Learning Research - Proceedings Track 2: 412-419 (2007)
j39		Ilya Sutskever, Geoffrey E. Hinton: Learning Multilevel Distributed Representations for High-Dimensional Sequences. Journal of Machine Learning Research - Proceedings Track 2: 548-555 (2007)
j38		Geoffrey E. Hinton: Boltzmann machine. Scholarpedia 2(5): 1668 (2007)
c66		Roland Memisevic, Geoffrey E. Hinton: Unsupervised Learning of Image Transformations. CVPR 2007
c65		Andriy Mnih, Geoffrey E. Hinton: Three new graphical models for statistical language modelling. ICML 2007: 641-648
c64		Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton: Restricted Boltzmann machines for collaborative filtering. ICML 2007: 791-798
c63		Simon Osindero, Geoffrey E. Hinton: Modeling image patches with a directed hierarchy of Markov random fields. NIPS 2007
c62		Ruslan Salakhutdinov, Geoffrey E. Hinton: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. NIPS 2007
	2006
j37		Geoffrey E. Hinton, Simon Osindero, Max Welling, Yee Whye Teh: Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation. Cognitive Science 30(4): 725-731 (2006)
j36		Simon Osindero, Max Welling, Geoffrey E. Hinton: Topographic Product Models Applied to Natural Scene Statistics. Neural Computation 18(2): 381-414 (2006)
j35		Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh: A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18(7): 1527-1554 (2006)
c61		Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis: Modeling Human Motion Using Binary Latent Variables. NIPS 2006: 1345-1352
	2005
j34		Roland Memisevic, Geoffrey E. Hinton: Improving dimensionality reduction with spectral gradient descent. Neural Networks 18(5-6): 702-710 (2005)
c60		Geoffrey E. Hinton: What kind of graphical model is the brain? IJCAI 2005: 1765-
c59		Geoffrey E. Hinton, Vinod Nair: Inferring Motor Programs from Images of Handwritten Digits. NIPS 2005
	2004
j33		Brian Sallans, Geoffrey E. Hinton: Reinforcement Learning with Factored States and Actions. Journal of Machine Learning Research 5: 1063-1088 (2004)
j32		Max Welling, Richard S. Zemel, Geoffrey E. Hinton: Probabilistic sequential independent components analysis. IEEE Transactions on Neural Networks 15(4): 838-849 (2004)
c58		Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov: Neighbourhood Components Analysis. NIPS 2004
c57		Roland Memisevic, Geoffrey E. Hinton: Multiple Relational Embedding. NIPS 2004
c56		Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton: Exponential Family Harmoniums with an Application to Information Retrieval. NIPS 2004
	2003
j31		Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton: Energy-Based Models for Sparse Overcomplete Representations. Journal of Machine Learning Research 4: 1235-1260 (2003)
c55		Geoffrey E. Hinton, Max Welling, Andriy Mnih: Wormholes Improve Contrastive Divergence. NIPS 2003
c54		Max Welling, Richard S. Zemel, Geoffrey E. Hinton: Efficient Parametric Projection Pursuit Density Estimation. UAI 2003: 575-582
	2002
j30		Fiora Pirri, Geoffrey E. Hinton, Hector J. Levesque: In Memory of Ray Reiter (1939-2002). AI Magazine 23(4): 93 (2002)
j29		Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton: Local Physical Models for Interactive Character Animation. Comput. Graph. Forum 21(3): 337-346 (2002)
j28		Geoffrey E. Hinton: Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation 14(8): 1771-1800 (2002)
j27		Guy Mayraz, Geoffrey E. Hinton: Recognizing Handwritten Digits Using Hierarchical Products of Experts. IEEE Trans. Pattern Anal. Mach. Intell. 24(2): 189-197 (2002)
c53		Sageev Oore, Demetri Terzopoulos, Geoffrey E. Hinton: A Desktop Input Device and Interface for Interactive 3D Character Animation. Graphics Interface 2002: 133-140
c52		Max Welling, Geoffrey E. Hinton: A New Learning Algorithm for Mean Field Boltzmann Machines. ICANN 2002: 351-357
c51		Max Welling, Richard S. Zemel, Geoffrey E. Hinton: Self Supervised Boosting. NIPS 2002: 665-672
c50		Geoffrey E. Hinton, Sam T. Roweis: Stochastic Neighbor Embedding. NIPS 2002: 833-840
c49		Max Welling, Geoffrey E. Hinton, Simon Osindero: Learning Sparse Topographic Representations with Products of Student-t Distributions. NIPS 2002: 1359-1366
	2001
j26		Alberto Paccanaro, Geoffrey E. Hinton: Learning Distributed Representations of Concepts Using Linear Relational Embedding. IEEE Trans. Knowl. Data Eng. 13(2): 232-244 (2001)
c48		Alberto Paccanaro, Geoffrey E. Hinton: Learning Hierarchical Structures with Linear Relational Embedding. NIPS 2001: 857-864
c47		Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton: Global Coordination of Local Linear Models. NIPS 2001: 889-896
c46		Andrew D. Brown, Geoffrey E. Hinton: Relative Density Nets: A New Way to Combine Backpropagation with HMM's. NIPS 2001: 1149-1156
c45		Geoffrey E. Hinton, Yee Whye Teh: Discovering Multiple Constraints that are Frequently Approximately Satisfied. UAI 2001: 227-234
	2000
j25		Zoubin Ghahramani, Geoffrey E. Hinton: Variational Learning for Switching State-Space Models. Neural Computation 12(4): 831-864 (2000)
j24		Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton: SMEM Algorithm for Mixture Models. Neural Computation 12(9): 2109-2128 (2000)
j23		Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton: Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. VLSI Signal Processing 26(1-2): 133-140 (2000)
c44		Geoffrey E. Hinton: Modeling High-Dimensional Data by Combining Simple Experts. AAAI/IAAI 2000: 1159-1164
c43		Alberto Paccanaro, Geoffrey E. Hinton: Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space. ICML 2000: 711-718
c42		Alberto Paccanaro, Geoffrey E. Hinton: Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions. IJCNN (2) 2000: 259-264
c41		Yee Whye Teh, Geoffrey E. Hinton: Rate-coded Restricted Boltzmann Machines for Face Recognition. NIPS 2000: 908-914
c40		Guy Mayraz, Geoffrey E. Hinton: Recognizing Hand-written Digits Using Hierarchical Products of Experts. NIPS 2000: 953-959
c39		Brian Sallans, Geoffrey E. Hinton: Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task. NIPS 2000: 1075-1081
	1999
j22		Brendan J. Frey, Geoffrey E. Hinton: Variational Learning in Nonlinear Gaussian Belief Networks. Neural Computation 11(1): 193-213 (1999)
c38		Geoffrey E. Hinton, Andrew D. Brown: Spiking Boltzmann Machines. NIPS 1999: 122-128
c37		Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh: Learning to Parse Images. NIPS 1999: 463-469
	1998
j21		S. Sidney Fels, Geoffrey E. Hinton: Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls. IEEE Transactions on Neural Networks 9(1): 205-212 (1998)
c36		Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton: SMEM Algorithm for Mixture Models. NIPS 1998: 599-605
c35		Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton: Fast Neural Network Emulation of Dynamical Systems for Computer Animation. NIPS 1998: 882-888
c34		Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton: NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models. SIGGRAPH 1998: 9-20
	1997
j20		Brendan J. Frey, Geoffrey E. Hinton: Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model. Comput. J. 40(2/3): 157-165 (1997)
j19		Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton: Instantiating Deformable Models with a Neural Net. Computer Vision and Image Understanding 68(1): 120-126 (1997)
j18		Peter Dayan, Geoffrey E. Hinton: Using Expectation-Maximization for Reinforcement Learning. Neural Computation 9(2): 271-278 (1997)
j17		Sageev Oore, Geoffrey E. Hinton, Gregory Dudek: A Mobile Robot that Learns its Place. Neural Computation 9(3): 683-699 (1997)
j16		Geoffrey E. Hinton, Peter Dayan, Michael Revow: Modeling the manifolds of images of handwritten digits. IEEE Trans. Neural Netw. Learning Syst. 8(1): 65-74 (1997)
j15		S. Sidney Fels, Geoffrey E. Hinton: Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls. IEEE Trans. Neural Netw. Learning Syst. 8(5): 977-984 (1997)
c33		Zoubin Ghahramani, Geoffrey E. Hinton: Hierarchical Non-linear Factor Analysis and Topographic Maps. NIPS 1997
	1996
j14		Peter Dayan, Geoffrey E. Hinton: Varieties of Helmholtz Machine. Neural Networks 9(8): 1385-1403 (1996)
j13		Michael Revow, Christopher K. I. Williams, Geoffrey E. Hinton: Using Generative Models for Handwritten Digit Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 592-606 (1996)
c32		Brendan J. Frey, Geoffrey E. Hinton: Free Energy Coding. Data Compression Conference 1996: 73-81
	1995
j12		Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel: The Helmholtz machine. Neural Computation 7(5): 889-904 (1995)
c31		Sidney Fels, Geoffrey E. Hinton: GloveTalkII: An Adaptive Gesture-to-Formant Interface. CHI 1995: 456-463
c30		Geoffrey E. Hinton, Michael Revow: Using Pairs of Data-Points to Define Splits for Decision Trees. NIPS 1995: 507-513
c29		Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan: Does the Wake-sleep Algorithm Produce Good Density Estimators? NIPS 1995: 661-667
	1994
c28		Lei Xu, Michael I. Jordan, Geoffrey E. Hinton: An Alternative Model for Mixtures of Experts. NIPS 1994: 633-640
c27		Sidney Fels, Geoffrey E. Hinton: Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks. NIPS 1994: 843-850
c26		Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton: Using a neural net to instantiate a deformable model. NIPS 1994: 965-972
c25		Geoffrey E. Hinton, Michael Revow, Peter Dayan: Recognizing Handwritten Digits Using Mixtures of Linear Models. NIPS 1994: 1015-1022
	1993
j11		Suzanna Becker, Geoffrey E. Hinton: Learning Mixture Models of Spatial Coherence. Neural Computation 5(2): 267-277 (1993)
j10		Steven J. Nowlan, Geoffrey E. Hinton: A soft decision-directed LMS algorithm for blind equalization. IEEE Transactions on Communications 41(2): 275-279 (1993)
c24		Geoffrey E. Hinton, Drew van Camp: Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. COLT 1993: 5-13
c23		Geoffrey E. Hinton, Richard S. Zemel: Autoencoders, Minimum Description Length and Helmholtz Free Energy. NIPS 1993: 3-10
c22		Richard S. Zemel, Geoffrey E. Hinton: Developing Population Codes by Minimizing Description Length. NIPS 1993: 11-18
	1992
j9		Steven J. Nowlan, Geoffrey E. Hinton: Simplifying Neural Networks by Soft Weight-Sharing. Neural Computation 4(4): 473-493 (1992)
c21		Peter Dayan, Geoffrey E. Hinton: Feudal Reinforcement Learning. NIPS 1992: 271-278
	1991
c20		Suzanna Becker, Geoffrey E. Hinton: Learning to Make Coherent Predictions in Domains with Discontinuities. NIPS 1991: 372-379
c19		Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow: Adaptive Elastic Models for Hand-Printed Character Recognition. NIPS 1991: 512-519
c18		Steven J. Nowlan, Geoffrey E. Hinton: Adaptive Soft Weight Tying using Gaussian Mixtures. NIPS 1991: 993-1000
	1990
j8		Geoffrey E. Hinton: Connectionist Symbol Processing - Preface. Artif. Intell. 46(1-2): 1-4 (1990)
j7		Geoffrey E. Hinton: Mapping Part-Whole Hierarchies into Connectionist Networks. Artif. Intell. 46(1-2): 47-75 (1990)
j6		Kevin J. Lang, Alex Waibel, Geoffrey E. Hinton: A time-delay neural network architecture for isolated word recognition. Neural Networks 3(1): 23-43 (1990)
c17		Sidney Fels, Geoffrey E. Hinton: Building adaptive interfaces with neural networks: The glove-talk pilot study. INTERACT 1990: 683-688
c16		Richard S. Zemel, Geoffrey E. Hinton: Discovering Viewpoint-Invariant Relationships That Characterize Objects. NIPS 1990: 299-305
c15		Steven J. Nowlan, Geoffrey E. Hinton: Evaluation of Adaptive Mixtures of Competing Experts. NIPS 1990: 774-780
	1989
j5		Geoffrey E. Hinton: Connectionist Learning Procedures. Artif. Intell. 40(1-3): 185-234 (1989)
c14		Kevin J. Lang, Geoffrey E. Hinton: Dimensionality Reduction and Prior Knowledge in E-Set Recognition. NIPS 1989: 178-185
c13		Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton: TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS 1989: 266-273
c12		Conrad C. Galland, Geoffrey E. Hinton: Discovering High Order Features with Mean Field Modules. NIPS 1989: 509-515
	1988
j4		David S. Touretzky, Geoffrey E. Hinton: A Distributed Connectionist Production System. Cognitive Science 12(3): 423-466 (1988)
c11		Yann LeCun, Conrad C. Galland, Geoffrey E. Hinton: GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection. NIPS 1988: 141-148
	1987
j3		Scott E. Fahlman, Geoffrey E. Hinton: Connectionist Architectures for Artificial Intelligence. IEEE Computer 20(1): 100-109 (1987)
c10		Geoffrey E. Hinton, James L. McClelland: Learning Representations by Recirculation. NIPS 1987: 358-366
c9		Geoffrey E. Hinton: Learning Translation Invariant Recognition in Massively Parallel Networks. PARLE (1) 1987: 1-13
	1986
c8		Drew V. McDermott, Geoffrey E. Hinton: Learning in Massively Parallel Nets (Panel). AAAI 1986: 1149
	1985
j2		David H. Ackley, Geoffrey E. Hinton, Terrence J. Sejnowski: A Learning Algorithm for Boltzmann Machines. Cognitive Science 9(1): 147-169 (1985)
c7		David S. Touretzky, Geoffrey E. Hinton: Symbols Among the Neurons: Details of a Connectionist Inference Architecture. IJCAI 1985: 238-243
c6		Geoffrey E. Hinton, Kevin J. Lang: Shape Recognition and Illusory Conjunctions. IJCAI 1985: 252-259
	1983
c5		Scott E. Fahlman, Geoffrey E. Hinton, Terrence J. Sejnowski: Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines. AAAI 1983: 109-113
	1981
c4		Geoffrey E. Hinton: A Parallel Computation that Assigns Canonical Object-Based Frames of Reference. IJCAI 1981: 683-685
c3		Geoffrey E. Hinton: Shape Representation in Parallel Systems. IJCAI 1981: 1088-1096
	1979
j1		Geoffrey E. Hinton: Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery. Cognitive Science 3(3): 231-250 (1979)
	1978
c2		Aaron Sloman, David Owen, Geoffrey E. Hinton, Frank Birch, Frank O'Gorman: Representation and Control in Vision. AISB/GI (ECAI) 1978: 309-314
	1976
c1		Geoffrey E. Hinton: Using Relaxation to find a Puppet. AISB (ECAI) 1976: 148-157

Coauthor Index

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