Using a neural net to instantiate a deformable model CKI Williams, M Revow, GE Hinton Advances in neural information processing systems, 965-972, 1995 | 6 | 1995 |
Hand-printed digit recognition using deformable models CKI Williams, GE Hinton Spatial Vision in Humans and Robots: The Proceedings of the 1991 York ..., 1993 | 6 | 1993 |
El atractivo del procesamiento distribuido en paralelo JL McClelland, DE Rumelhart, GE Hinton Introducción al procesamiento distribuido en paralelo, 1992 | 6 | 1992 |
Learning to make coherent predictions in domains with discontinuities S Becker, GE Hinton Advances in Neural Information Processing Systems, 372-379, 1992 | 6 | 1992 |
Learning Error Representation by Error Propagation DE Rumelhart, GE Hinton, RJ Williams Parallel Distributed Processing—Explorations in the Microstructure of ..., 1988 | 6 | 1988 |
The horizontal-vertical delusion GE Hinton Perception 16 (5), 677-680, 1987 | 6 | 1987 |
gParallel Distributed Proce ssing Vol. 1, 2• h DE Rumelhart, JL McClelland, PDP Group MIT Press, 1986 | 6 | 1986 |
Parallel Distributed Processing. Exploration of the Microstructure of Cognition. vol. 1: Foundations, DE Rumelhart and JL McClelland DE Rumelhart, GE Hinton, RJ Williams MIT Press, 1986 | 6 | 1986 |
Learning in massively parallel nets GE Hinton Proceedings of the AAAI 86, 1986 | 6 | 1986 |
Parallel stochastic search in early vision TJ Sejnowski, GE Hinton Johns Hopkins University. Electrical Engineering and Computer Science Department, 1984 | 6 | 1984 |
ins.''Learning internal representations by error propagation DE Rumelhalt, GE Hinton, RJ Willia Parallel Distributed Processing: Explorations in the Microstructure of, 0 | 6 | |
Using an autoencoder with deformable templates to discover features for automated speech recognition. N Jaitly, GE Hinton INTERSPEECH, 1737-1740, 2013 | 5 | 2013 |
Machine learning for neuroscience GE Hinton Neural Syst. Circuits 1 (1), 12, 2011 | 5 | 2011 |
Using matrices to model symbolic relationship I Sutskever, GE Hinton Advances in Neural Information Processing Systems, 1593-1600, 2009 | 5 | 2009 |
Probabilistic sequential independent components analysis M Welling, RS Zemel, GE Hinton Neural Networks, IEEE Transactions on 15 (4), 838-849, 2004 | 5 | 2004 |
Training many small hidden markov models GE Hinton, AD Brown WISP-2001 Workshop on Innovation in Speech Processing, Proc. Institute of ..., 2001 | 5* | 2001 |
Parameter estimation for linear dynamical systems. University of Toronto Technical Report Z Ghahramani, GE Hinton CRG-TR-96-2, 1996 | 5 | 1996 |
Neural networks for computer-human interfaces: Glove-TalkII SS Fels, G Hinton Proceedings of the International Conference on Neural Information Processing ..., 1996 | 5 | 1996 |
Combining two methods of recognizing hand-printed digits G Hinton, C Williams, M Revow Art. Neural Systems 2, 53-60, 1992 | 5 | 1992 |
Neural network architectures for artificial intelligence GE Hinton American Association for Artificial Intelligence, 1988 | 5 | 1988 |
Pattern matching and variable binding in a stochastic neural network DS Touretzky, GE Hinton Genetic Algorithms and Simulated Annealing, 155-169, 1987 | 5 | 1987 |
Learning internal representations by error propagation, Parallel Distributed Processing (DE Rumelhart and JL McClelland, Eds.), 1,318-362 DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, MA, 1986 | 5 | 1986 |
Models of schemata and sequential thought processes DE Rumelhart, P Smolensky, JL McClelland, GE Hinton Parallel Distributed Processing: Explorations in the microstructure of ..., 1986 | 5 | 1986 |
Learning internal representation by error propagation. Parallel Distributed Processing, Chapter 8 DE Rumelhart, GE Hinton, RJ Williams MIT Press, 1986 | 5 | 1986 |
ªLearning Internal Representation by Error Propagation, º Parallel Distributed Processing DE Rumelhart, GE Hinton, R Williams DE Rumelhart, JL McClelland, and the PDP Research Group, eds 1, 283-317, 1986 | 5 | 1986 |
Learning internal representation by error propagation, Parallel Distributed Processing, Vol. 1: foundations, DE Rumelhart and JL McClelland, eds DE Rumelhart, GE Hinton, RJ Williams MIT Press, Cambridge, Mass, 1986 | 5 | 1986 |
Distributed Representations. DE Rumelhart and JL McClelland (Eds) Parallel Distributed Processing: Explorations in the microstructure of cognition. Volume I: foundations (pp. 77-109) GE Hinton Cambridge MA: MIT Press, 1986 | 5 | 1986 |
Parallel Data Processing, vol. 1 D Rumelhart, J McClelland The MIT Press, Cambridge, MA, 1986 | 5 | 1986 |
Learning internal representations by error propagation. 1985 DE Rumelhart, GE Hinton, RJ Williams DTIC Document, 0 | 5 | |
Reducing the Dimensionality of Data with Neural Networks.(28 July 2006) GE Hinton, RR Salakhutdinov Science 313 (5786), 504, 0 | 5 | |
R.]. Williams, 1986. Learning internal representations by error propagation, Parallel Distributed Processing—Explorations in the Microstructure of Cognition, Vol. 1 (DE. Rumelhart,]. L. McClelland, and PDP Research Group, editors) DE Rumelhart, GE Hinton The MIT Press, Massachusetts, 0 | 5 | |
Learning nonlinear constraints with contrastive backpropagation A Mnih, G Hinton Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint ..., 2005 | 4 | 2005 |
Relative Density Nets: A New Way to Combine Backpropagation with HMM's AD Brown, GE Hinton Advances in Neural Information Processing Systems, 1149-1156, 2001 | 4 | 2001 |
Modeling High-Dimensional Data by Combining Simple Experts GE Hinton AAAI/IAAI, 1159-1164, 2000 | 4 | 2000 |
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space. A Paccanaro, GE Hinton ICML, 711-718, 2000 | 4 | 2000 |
Learning Internal Representations by Error Propagation", Rumelhart D., McClelland J.," Parallel Distributed Processing: Explorations in the Microstructure of Cognition", I & II D Rumelhart, G Hinton, R Williams MIT Press, Cambridge MA, 1986 | 4 | 1986 |
Learning internal representations by error propagation, parallel distributed processing Cambridge: MA DE Rumelhart, GE Hinton, RJ Williams MIT Press, 1986 | 4 | 1986 |
Williams ILl, 1986b. Learning internal representations by error propagation DE Rumelhart, GE Hinton Parallel Distributed Processing: Exploration in the Microstrueture of ..., 0 | 4 | |
8: McClel1and, JL (I986). A general framework for parallel distributed processing DE Rumelhart, GE Hinton Parallel distributed processing: Explorations in the microstructure of ..., 0 | 4 | |
DELVE team members (1995). Assessing learning procedures using DELVE G Hinton, R Neal, R Tibshirani Technical report, University of Toronto, Department of Computer Science, 0 | 4 | |
Delve data for evaluating learning in valid experiments, 1995–1996 CE Rasmussen, RM Neal, G Hinton, D van Camp, M Revow, ... URL http://www. cs. toronto. edu/∼ delve, 0 | 4 | |
Efficient Parametric Projection Pursuit Density Estimation MWRSZ Geoffrey, E Hinton | 4* | |
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units QV Le, N Jaitly, GE Hinton arXiv preprint arXiv:1504.00941, 2015 | 3 | 2015 |
A new way to learn acoustic events N Jaitly, GE Hinton Advances in Neural Information Processing Systems 24, 2011 | 3 | 2011 |
How to do backpropagation in a brain G Hinton Invited talk at the NIPS’2007 Deep Learning Workshop, 2007 | 3 | 2007 |
Deep Belief Networks R Salakhutdinov, GE Hinton | 3 | 2007 |
Scaling in a hierarchical unsupervised network Z Ghahramani, AT Korenberg, GE Hinton Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference ..., 1999 | 3 | 1999 |
Cascaded redundancy reduction VR De Sa, GE Hinton Network: Computation in Neural Systems 9 (1), 73-84, 1998 | 3 | 1998 |
Minimizing description length in an unsupervised neural network GE Hinton, RS Zemel Preprint, 1997 | 3 | 1997 |
Ch8: Learning Internal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition Vol. 1 Foundations DE Rumelhart, GE Hinton, RJ Williams The MIT Press pp318-362, 1995 | 3 | 1995 |
Advances in neural information processing systems C Linster, D Marsan, C Masson, M Kerszberg San Francisco, CA: Morgan Kaufmann, 1994 | 3 | 1994 |
Learning representations by back-propagating errors (from Nature 1986) DE Rumelhart, GE Hinton, RJ Williams Spie Milestone Series Ms 96, 138-138, 1994 | 3 | 1994 |
Proceedings of the 1988 Connectionist Models Summer School DS Touretzky, GE Hinton, TJ Sejnowski Morgan Kaufmann, 1989 | 3 | 1989 |
Parallel computation and the mass-spring model of motor control GE Hinton, P Smolensky Cognitive Science Laboratory, Center for Human Information Processing ..., 1984 | 3 | 1984 |
Generative multiple-instance learning models for quantitative electromyography T Adel, B Smith, R Urner, D Stashuk, DJ Lizotte arXiv preprint arXiv:1309.6811, 2013 | 2 | 2013 |
A comparison of classification methods for longitudinal fmri studies T Schmah, G Yourganov, RS Zemel, GE Hinton, SL Small, S Strother Neuroimage 47, S57, 2009 | 2 | 2009 |
Improving a statistical language model by modulating the effects of context words. Z Yuecheng, A Mnih, GE Hinton ESANN, 493-498, 2008 | 2 | 2008 |
Embedding via clustering: Using spectral information to guide dimensionality reduction R Memisevic, G Hinton Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint ..., 2005 | 2 | 2005 |
Learning distributed representations of relational data using linear relational embedding A Paccanaro, GE Hinton Neural Nets WIRN Vietri-01, 134-143, 2002 | 2 | 2002 |
DC n, A. Kyker, and P. Roussel. The microarchitecture of the pentium 4 processor G Hinton, D Sager, M Upton, D Boggs Intel Technical Journal Q 1, 2001, 2001 | 2 | 2001 |
Fast neural network emulation of dynamical systems for computer animation R Grzeszczuk, D Terzopoulos, GE Hinton Advances in neural information processing systems, 882-888, 1999 | 2 | 1999 |
Phoneme recognition with a neural network: comparisons of acoustic representations including those produced by an auditory model WC Treurniet, MJ Hunt, C Lefebvre, Z Jacobson Neural Networks 1, 320, 1988 | 2 | 1988 |
A distributed connectionist production system GE Hinton, DS Touretzky Cognitive science 12 (3), 1988 | 2 | 1988 |
Parallel distributed processing. Vol. 1 GE Hinton, TJ Sejnowski Cambridge, MA: MIT Press, 1986 | 2 | 1986 |
Learning internal representations by error propagation. I” D. Rumelhart & J. McClelland (Eds.), Parallel djrlribuledprocessing: Expiorotions in the micros~ ucrure ofcognition. Vol. I; Foundokms D Rumelhart, G Hinton, R Williams Cambridge, MA: MIT Press, 1986 | 2 | 1986 |
Respectively Reconsidered G Hinton Pragmatics Microfiche 3, 912-914, 1978 | 2 | 1978 |
Wfll] ams, KJ: Learning internal representations by error propagation DE Rumelhart, GE Hinton Parallel Distributed Processing 1, 0 | 2 | |
Modelling the statistics of natural images with topographic product of student-t models S Osindero, M Welling, GE Hinton Neural Computation 18 (2), 2006 | 1 | 2006 |
15 Learning to Use Spike Timing in a Restricted Boltzmann Machine GE Hinton, AD Brown Probabilistic Models of the Brain, 285, 2002 | 1 | 2002 |
Variational Learning for Switching State-Space Models GE Hinton Graphical Models: Foundations of Neural Computation, 315, 2001 | 1 | 2001 |
Pattern classification using a mixture of factor analyzers N Ueda, R Nakano, Z Ghahramani, G Hinton Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE ..., 1999 | 1 | 1999 |
Using mixtures of factor analyzers for segmentation and pose estimation GE Hinton, M Revow Unpublished, 1998 | 1 | 1998 |
E cient stochastic source coding and an application to a Bayesian network source model BJ Frey, GE Hinton Computer Journal, 1997 | 1 | 1997 |
Using neural networks to monitor for rare failures GE Hinton, BJ Frey MECHANICAL WORKING AND STEEL PROCESSING CONFERENCE PROCEEDINGS, 545-548, 1996 | 1 | 1996 |
Using neural networks to learn intractable generative models GE HINTON, P Dayan, RM NEAL, RS ZEMEL AMER STATISTICAL ASSOC, 1994 | 1 | 1994 |
The unity of consciousness: A connectionist account GE Hinton Memories, Thoughts, and Emotions: Essays in Honor of George Mandler, 245, 1991 | 1 | 1991 |
Theoretical psychology, artificial intelligence, and empirical research. A Kukla American Psychological Association 45 (6), 780, 1990 | 1 | 1990 |
SCENE-BASED AND VIEWER-CENTERED REPRESENTATIONS FOR COMPARING SHAPE LM Parsons, GE HINTON BULLETIN OF THE PSYCHONOMIC SOCIETY 26 (6), 510-510, 1988 | 1 | 1988 |
Models of human inference GE Hinton Computational intelligence 3 (1), 189-190, 1987 | 1 | 1987 |
Guest Editorial: Deep Learning G Hinton, Y LeCun International Journal of Computer Vision 113 (1), 1-2, 2015 | | 2015 |
Dimensionality Reduction G Hinton | | 2015 |
time (min.) DE Rumelhart, GE Hinton, RJ Williams Advanced Control of Chemical Processes 1994 45, 111, 2014 | | 2014 |
Deterministic Boltzmann Learning in Networks with Asymmetric Connectivity GE Hinton Connectionist Models: Proceedings of the 1990 Summer School, 3, 2014 | | 2014 |
Updated Edition GE Hinton Parallel Models of Associative Memory: Updated Edition, 1, 2014 | | 2014 |
System and method for parallelizing convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton US Patent App. 14/030,938, 2013 | | 2013 |
System and method for addressing overfitting in a neural network GE Hinton, A Krizhevsky, I Sutskever, N Srivastva US Patent App. 14/015,768, 2013 | | 2013 |
System and method for generating training cases for image classification A Krizhevsky, I Sutskever, GE Hinton US Patent App. 13/970,869, 2013 | | 2013 |
System and method for labelling aerial images V Mnih, GE Hinton US Patent App. 13/924,320, 2013 | | 2013 |
Modeling Semantic Similarities in Multiple Maps L van der Maaten, G Hinton | | 2009 |
Workshop summary: Workshop on learning feature hierarchies K Yu, R Salakhutdinov, Y LeCun, G Hinton, Y Bengio Proceedings of the 26th Annual International Conference on Machine Learning, 5, 2009 | | 2009 |
Deep Generative Models for Modeling Animate Motion GW Taylor, GE Hinton, S Roweis Proc. 4th Int. Symp. Adaptive Motion of Animals and Machines, 2008 | | 2008 |
Copyright© 2006 Cognitive Science Society, Inc. All rights reserved. K Abbot-Smith, S Atran, M Aveyard, H Behrens, S Benus, L Blomert, ... Cognitive Science 30, 1127, 2006 | | 2006 |
Wormholes Improve Contrastive Divergence M Welling, A Mnih, GE Hinton Advances in Neural Information Processing Systems, None, 2003 | | 2003 |
15 Learning to Use Spike Timing in a Restricted B Machine, GE Hinton, AD Brown Probabilistic Models of the Brain: Perception and Neural Function, 285, 2002 | | 2002 |
Nonlinear Dimensionality Reduction using Neural Networks R Salakhutdinov RBM 2, 1000, 2000 | | 2000 |
Learning mixture models of spatial coherence GE Hinton Unsupervised Learning: Foundations of Neural Computation, 223, 1999 | | 1999 |
Learning Population Codes by Minimizing Description GE Hinton Unsupervised Learning: Foundations of Neural Computation, 261, 1999 | | 1999 |
Department of Computer Science University of Toronto Toronto, Ontario, M5S 1A4, Canada hinton@ cs. toronto. edu, zoubin@ cs. toronto. edu GE Hinton, Z Ghahramani | | 1997 |
Acharya, A., see Chakrabarti, PP. Adam, A. and J.-P Laurent, LAURA, a system to debug stu-dent programs Adcock, J., see Char&k, E. Addanki, S., R. Cremonini and JS Penberthy, Graphs of mod GM Adelson-Velskiy, VL Arlazarov, MV Donskoy, TS Elman, G Hinton Artificial Intelligence 96, 229-299, 1997 | | 1997 |
Learning fast neural network emulators for physics-based models R Grzezczuk, D Terzopoulos, G Hinton ACM SIGGRAPH 97 Visual Proceedings: The art and interdisciplinary programs ..., 1997 | | 1997 |