Deep belief nets for natural language call-routing R Sarikaya, GE Hinton, B Ramabhadran Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International ..., 2011 | 31 | 2011 |
Learning distributed representations of concepts using linear relational embedding A Paccanaro, GE Hinton Knowledge and Data Engineering, IEEE Transactions on 13 (2), 232-244, 2001 | 31 | 2001 |
Multiple relational embedding R Memisevic, GE Hinton Advances in neural information processing systems, 913-920, 2004 | 30 | 2004 |
Spiking boltzmann machines GE Hinton, A Brown Advances in neural information processing systems 12: proceedings of the ..., 2000 | 30* | 2000 |
The bootstrap Widrow-Hoff rule as a cluster-formation algorithm GE Hinton, SJ Nowlan Neural Computation 2 (3), 355-362, 1990 | 30 | 1990 |
Some computational solutions to Bernstein's problems G Hinton Human Motor Actions—Bernstein Reassessed, 413-438, 1984 | 30 | 1984 |
A 0.18/spl mu/m CMOS IA32 microprocessor with a 4 GHz integer execution unit D Sager, G Hinton, M Upton, T Chappell, TD Fletcher, S Samaan, ... Solid-State Circuits Conference, 2001. Digest of Technical Papers. ISSCC ..., 2001 | 29 | 2001 |
Learning to Parse Images. GE Hinton, Z Ghahramani, YW Teh NIPS, 463-469, 1999 | 29 | 1999 |
Delve data for evaluating learning in valid experiments CE Rasmussen, RM Neal, G Hinton, D van Camp, M Revow, ... URL http://www. cs. toronto. edu/∼ delve, 1996 | 29 | 1996 |
Comparing classification methods for longitudinal fMRI studies T Schmah, G Yourganov, RS Zemel, GE Hinton, SL Small, SC Strother Neural Computation 22 (11), 2729-2762, 2010 | 28 | 2010 |
Discovering multiple constraints that are frequently approximately satisfied GE Hinton, YW Teh Proceedings of the Seventeenth conference on Uncertainty in artificial ..., 2001 | 28 | 2001 |
Hierarchical non-linear factor analysis and topographic maps Z Ghahramani, GE Hinton Advances in neural information processing systems, 486-492, 1998 | 28 | 1998 |
Gated softmax classification R Memisevic, C Zach, M Pollefeys, GE Hinton Advances in Neural Information Processing Systems, 1603-1611, 2010 | 27 | 2010 |
Unsupervised discovery of nonlinear structure using contrastive backpropagation G Hinton, S Osindero, M Welling, YW Teh Cognitive science 30 (4), 725-731, 2006 | 27 | 2006 |
Local physical models for interactive character animation S Oore, D Terzopoulos, G Hinton Computer Graphics Forum 21 (3), 337-346, 2002 | 26 | 2002 |
Dimensionality reduction and prior knowledge in E-set recognition KJ Lang, GE Hinton Advances in neural information processing systems, 178-185, 1990 | 26 | 1990 |
TRAFFIC: Recognizing objects using hierarchical reference frame transformations RS Zemel, MC Mozer, GE Hinton Advances in neural information processing systems, 266-273, 1990 | 25 | 1990 |
Visualizing non-metric similarities in multiple maps L Van der Maaten, G Hinton Machine learning 87 (1), 33-55, 2012 | 24 | 2012 |
Learning to label aerial images from noisy data V Mnih, GE Hinton Proceedings of the 29th International Conference on Machine Learning (ICML ..., 2012 | 24 | 2012 |
Learning causally linked markov random fields GE Hinton, S Osindero, K Bao Proceedings of the 10th International Workshop on Artificial Intelligence ..., 2005 | 23 | 2005 |
What kind of graphical model is the brain? GE Hinton IJCAI 5, 1765-1775, 2005 | 22 | 2005 |
Using free energies to represent Q-values in a multiagent reinforcement learning task B Sallans, GE Hinton NIPS, 1075-1081, 2000 | 22 | 2000 |
A new view of ICA GE Hinton, M Welling, YW Teh, S Osindero Int. Conf. on Independent Component Analysis and Blind Source Separation, 2001 | 21 | 2001 |
Application of deep belief networks for natural language understanding R Sarikaya, GE Hinton, A Deoras Audio, Speech, and Language Processing, IEEE/ACM Transactions on 22 (4), 778-784, 2014 | 20 | 2014 |
Self supervised boosting M Welling, RS Zemel, GE Hinton Advances in Neural Information Processing Systems, 665-672, 2002 | 20 | 2002 |
An unsupervised learning procedure that discovers surfaces in random-dot stereograms GE Hinton, S Becker Proceedings of the International Joint Conference on Neural Networks ..., 1990 | 20 | 1990 |
Why the islands move E Hutchins, GE Hinton Perception 13 (5), 629-632, 1984 | 20 | 1984 |
Grammar as a foreign language O Vinyals, L Kaiser, T Koo, S Petrov, I Sutskever, G Hinton arXiv preprint arXiv:1412.7449, 2014 | 19 | 2014 |
Modeling natural images using gated MRFs MA Ranzato, V Mnih, JM Susskind, GE Hinton Pattern Analysis and Machine Intelligence, IEEE Transactions on 35 (9), 2206 ..., 2013 | 19 | 2013 |
Vocal tract length perturbation (VTLP) improves speech recognition N Jaitly, GE Hinton Proc. ICML Workshop on Deep Learning for Audio, Speech and Language, 2013 | 19 | 2013 |
Learning Generative Texture Models with extended Fields-of-Experts. N Heess, CKI Williams, GE Hinton BMVC, 1-11, 2009 | 19 | 2009 |
PDP: Computational models of cognition and perception, I DE Rumelhart, GE Hinton, RJ Williams I, chapter Learning internal representations by error propagation, 319-362, 1986 | 18 | 1986 |
Imagery without arrays G Hinton Behavioral and Brain Sciences 2 (04), 555-556, 1979 | 18 | 1979 |
Coaching variables for regression and classification R Tibshirani, G Hinton Statistics and Computing 8 (1), 25-33, 1998 | 17 | 1998 |
A simple algorithm that discovers efficient perceptual codes BJ Frey, P Dayan, GE Hinton Computational and psychophysical mechanisms of visual coding, 296-315, 1997 | 17 | 1997 |
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection. Y LeCun, CC Galland, GE Hinton NIPS, 141-148, 1988 | 17 | 1988 |
Learning internal representations by error propagation. Pararell Distributed processing: Explorations in the microestructure of cognition. 1-3 D Rumelhart, J McClelland, PDP Research Group MIT Press, 1986 | 17 | 1986 |
Connectionist Models: Proceedings of the 1990 Summer School Morgan Kaufmann, 2014 | 16* | 2014 |
Keeping neural networks simple GE Hinton, D van Camp ICANN’93, 11-18, 1993 | 16 | 1993 |
Deterministic Boltzmann learning in networks with asymmetric connectivity CC Galland, GE Hinton Connectionist Models: Proceedings of the 1990 Summer School, 3-9, 1991 | 16 | 1991 |
Schemata and sequential thought processes in PDP models, Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models DE Rumelhart, P Smolensky, JL McClelland, GE Hinton Chicago: Psychological and Biological Models, 1986 | 16 | 1986 |
Learning Sequential Structure in Simple Recurrent Networks DE Rumelhart, G Hinton, R Williams Parallel distributed processing: Experiments in the microstructure of ..., 1986 | 16 | 1986 |
Leaming in-ternal representations by error propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing: Explorations in the Microstructure of ..., 0 | 16 | |
Deep mixtures of factor analysers Y Tang, R Salakhutdinov, G Hinton arXiv preprint arXiv:1206.4635, 2012 | 15 | 2012 |
A mode-hopping MCMC sampler C Sminchisescu, M Welling, G Hinton Technical Report CSRG-478, University of Toronto, submitted to Machine ..., 2003 | 15 | 2003 |
Parallel Distributed Processing, DE Rumelhart and JL McClelland DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA, 1986 | 15 | 1986 |
Learning internal representations by error propagation, Parallel Distributed Processing, vol. 1, 318-362 DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, 1986 | 15 | 1986 |
Learning Internal Representations by Error Propagation. Parallel Distributed Processing, Vol. I, Rumelhart, D. E. and McClelland, JL DE Rumelhart, GE Hinton, JR Williams MIT Press, Cambridge, MA, 1986 | 14 | 1986 |
Learning internal representations by error propagation, volume 1 of Parallel Distributed Processing DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA, 1986 | 14 | 1986 |
Using relaxation to find a puppet GE Hinton Proc. of the AISB Summer Conference, 148-157, 1976 | 14 | 1976 |
How neural networks learn from experience GE Hinton Cognitive Modeling 267, 181-195, 2002 | 13 | 2002 |
Instantiating deformable models with a neural net CKI Williams, M Revow, GE Hinton Computer vision and image understanding 68 (1), 120-126, 1997 | 13 | 1997 |
Temporal-kernel recurrent neural networks I Sutskever, G Hinton Neural Networks 23 (2), 239-243, 2010 | 12 | 2010 |
The ups and downs of Hebb synapses. G Hinton Canadian Psychology/Psychologie canadienne 44 (1), 10, 2003 | 12 | 2003 |
Using pairs of data-points to define splits for decision trees GE Hinton, M Revow Advances in Neural Information Processing Systems, 1996 | 12 | 1996 |
The role of spatial working memory in shape perception GE Hinton Third Annual Conference of the Cognitive Science Society, Berkeley, 1981 | 12 | 1981 |
Where do features come from? G Hinton Cognitive science 38 (6), 1078-1101, 2014 | 11 | 2014 |
Generalized Darting Monte Carlo. C Sminchisescu, M Welling, G Hinton AISTATS, 516-523, 2007 | 11 | 2007 |
Wormholes improve contrastive divergence G Hinton, M Welling, A Mnih Advances in Neural Information Processing Systems 16, 417-424, 2004 | 11 | 2004 |
Extracting distributed representations of concepts and relations from positive and negative propositions A Paccanaro, GE Hinton Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS ..., 2000 | 11 | 2000 |
Learning Internal Representations by Error Backpropagation in Parallel Distributed Processing DE Rumelhart, GE Hinton, RJ Williams Exploration of Microstructure of Cognition 1, 1986 | 11 | 1986 |
Products of hidden Markov models: It takes N> 1 to tango GW Taylor, GE Hinton Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial ..., 2009 | 10 | 2009 |
Analysis-by-synthesis by learning to invert generative black boxes V Nair, J Susskind, GE Hinton Artificial Neural Networks-ICANN 2008, 971-981, 2008 | 10 | 2008 |
Data for evaluating learning in valid experiments (delve) CE Rasmussen, RM Neal, G Hinton, D Camp, M Revow, Z Ghahramani, ... | 10 | 2003 |
Efficient stochastic source coding and an application to a Bayesian network source model BJ Frey, GE Hinton The Computer Journal 40 (2 and 3), 157-165, 1997 | 10 | 1997 |
Neural network-based retrieval from software reuse repositories D Eichmann, K Srinivas Neural Networks and Pattern Recognition in Human Computer Interaction, R ..., 1992 | 10 | 1992 |
Discovering high order features with mean field modules CC Galland, GE Hinton Advances in neural information processing systems 2, 509-515, 1990 | 10 | 1990 |
Learning internal representations by error propagation, Neurocomputing: foundations of research DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA, 1988 | 10 | 1988 |
Learning representations by back-propagation errors DE Ruhmelhart, GE Hinton, RJ Wiliams Nature 323, 533-536, 1986 | 10 | 1986 |
Schemata and sequential thought processes in PDP models, Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models DE Rumlehart, P Smolensky, JL McClelland, GE Hinton MIT Press, Cambridge, MA, 1986 | 10 | 1986 |
Error Propagation DE Rummelhart, GE Hinton, RJWLIR By Parallel Distributed Processing, 0 | 10 | |
Introduction to the special section on deep learning for speech and language processing D Yu, G Hinton, N Morgan, JT Chien, S Sagayama Audio, Speech, and Language Processing, IEEE Transactions on 20 (1), 4-6, 2012 | 9 | 2012 |
Learning hierarchical structures with linear relational embedding APGE Hinton Advances in neural information processing systems 14: proceedings of the ..., 2002 | 9 | 2002 |
Computation by neural networks GE Hinton nature neuroscience 3, 1170-1170, 2000 | 9 | 2000 |
The Helmholtz machine through time G Hinton, P Dayan, A To, R Neal Proceedings of the ICANN, 483-490, 1995 | 9 | 1995 |
Using mixtures of deformable models to capture variations in hand printed digits M Revow, CKI Williams, GE Hinton | 9 | 1993 |
Inferring the meaning of direct perception GE Hinton Behavioral and Brain Sciences 3 (03), 387-388, 1980 | 9 | 1980 |
K., Lang, K., D.(1989). Phoneme recognition using time-delay neural networks A Waibel, H Toshiyuki, GS Hinton IEEE Transactions on Acoustics, Speech, and Signal Processing 23, 993-1009, 0 | 9 | |
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models N Jaitly, V Vanhoucke, G Hinton Proc. Annual Conference of International Speech Communication Association ..., 2014 | 8 | 2014 |
Improving dimensionality reduction with spectral gradient descent R Memisevic, G Hinton Neural networks 18 (5), 702-710, 2005 | 8 | 2005 |
Technical Report CRG-TR-96-2 Z Ghahramani, GE Hinton Department of Computer Science, University of Toronto, 1996 | 8 | 1996 |
Phoneme recognition using time-delay neural network G Hinton, A Waibel IEEE transactions on acoustics, speech, and signal processing 37, 328-339, 1989 | 8 | 1989 |
TRAFFIC: A model of object recognition based on transformations of feature instances RS Zemel, MC Mozer, GE Hinton Proceedings of the 1988 Connectionist Summer School, Morgan Kauffman: Los ..., 1988 | 8 | 1988 |
Learning Internal Representations by Error Propagation, Vol. 1 Parallel Distributed Processing: Exploration in the Microstructure of Cognition DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA, 1986 | 8 | 1986 |
Learning internal representations by error propagation, parallel distributed processing DE Rumlhart, GE Hinton, RJ Wiliams Cambridge, MA: MIT Press, 1986 | 8 | 1986 |
Novice use of an interactive graph plotting system N Hammond, A MacLean, G Hinton, J Long, P Barnard, IA Clark IBM Hursley Human Factors Report# HF083. IBM UK Laboratories Ltd., Hursley ..., 1983 | 8 | 1983 |
A better way to learn features: technical perspective GE Hinton Communications of the ACM 54 (10), 94-94, 2011 | 7 | 2011 |
Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine. MD Zeiler, GW Taylor, NF Troje, GE Hinton ESANN, 2009 | 7 | 2009 |
Free energy coding BJ Frey, GE Hinton Data Compression Conference, 1996. DCC'96. Proceedings, 73-81, 1996 | 7 | 1996 |
ican Society of Mechanical Engineers, ASME Press, 1992. SJ Hanson, RP Lippmann, DE Rumelhart, GE Hinton, RJ Williams | 7 | 1994 |
Une nouvelle approche de la cognition: le connexionnisme JL McClelland, DE Rumelhart, GE Hinton Le Débat 47 (5), 45-64, 1987 | 7 | 1987 |
Phoneme recognition using time-delay neural networks (Technical Report TR-I-0006) A Waibel, T Hanazawa, G Hinton, K Shikano, K Lang Japan: Advanced Telecommunications Research Institute, 1987 | 7 | 1987 |
Learning representations by back-propagating correlations DE Rumelhart, GE Hinton, RJ Williams Nature 333, 533-536, 1986 | 7 | 1986 |
Learning internal representation by error propagation, Parallel Distributed Processing, DE Rumelhart and JL McClelland, eds DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, 1986 | 7 | 1986 |
Parallel Distributed Processing, 1, 318 DE Rumelhart, GE Hinton, RJ Williams Cambridge, MA: MIT Press, 1986 | 7 | 1986 |
Tensor analyzers Y Tang, R Salakhutdinov, G Hinton Proceedings of The 30th International Conference on Machine Learning, 163-171, 2013 | 6 | 2013 |
Deep lambertian networks Y Tang, R Salakhutdinov, G Hinton arXiv preprint arXiv:1206.6445, 2012 | 6 | 2012 |
Deep belief nets G Hinton Encyclopedia of Machine Learning, 267-269, 2010 | 6 | 2010 |
Improving a statistical language model through non-linear prediction A Mnih, Z Yuecheng, G Hinton Neurocomputing 72 (7), 1414-1418, 2009 | 6 | 2009 |
SMEM algorithm for mixture models U Naonori, N Ryohei, Z Ghahramani, GE Hinton Neural Computation 12 (9), 2109-2128, 2000 | 6 | 2000 |