Geoffrey E. Hinton's Publications
in Reverse Chronological Order
2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983-1976
2021 |
Hinton, G. E. How to represent part-whole hierarchies in a neural network [pdf] |
2021 |
Sun, W., Tagliasacchi, A., Deng, B., Sabour, S., Yazdani, S., Hinton,
G. E., Yi, K. M. Canonical Capsules: Unsupervised Capsules in Canonical Pose arXiv preprint arXiv:2012.04718 [pdf] |
2021 |
Sabour, S., Tagliasacchi, A., Yazdani, S., Hinton, G. E., Fleet, D. J. Unsupervised part representation by Flow Capsules arXiv preprint arXiv:2011.13920 [pdf] |
2020 |
Deng, B., Lewis, J. P., Jeruzalski, T., Pons-Moll, G., Hinton, G. E.,
Norouzi, M., Tagliasacchi, A. NASA: Neural Articulated Shape Approximation ECCV [pdf] |
2020 |
Müller, R., Kornblith, S., Hinton, G. E. Subclass distillation arXiv preprint arXiv:2002.03936 [pdf] |
2020 |
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. E. Big Self-Supervised Models are Strong Semi-Supervised Learners 34th Conference on Neural Information Processing Systems (NeurIPS 2020) [pdf] |
2020 |
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E. A Simple Framework for Contrastive Learning of Visual Representations Proceedings of the 37th International Conference on Machine Learning. Eds. Hal Daume III and Aarti Singh, pp 1597--1607. [pdf] |
2020 |
Lillicrap, T. P., Santoro, A., Marris, C. J,. Akerman, C., and
Hinton, G. E. Backpropagation and the Brain Nature Reviews Neuroscience volume 21, pp 335--346. [pdf] |
2020 |
Qin, Y., Frosst, N., Sabour, S., Raffel, C., Cottrell, C. and Hinton, G. Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions ICLR-2020 [pdf] |
2019 |
Kosiorek, A. R., Sabour, S., Teh, Y. W. and Hinton, G. E. Stacked Capsule Autoencoders Advances in Neural Information Processing Systems 32 [pdf] |
2019 |
Zhang, M., Lucas, J., Ba, J., and Hinton, G. E. Lookahead Optimizer: k steps forward, 1 step back Advances in Neural Information Processing Systems 32 [pdf] |
2019 |
Müller, R., Kornblith, S. and Hinton G. When Does Label Smoothing Help? Advances in Neural Information Processing Systems 32 [pdf] |
2019 |
Deng, B., Kornblith, S. and Hinton, G. (2019) Cerberus: A multi-headed derenderer 3D Scene Understanding Workshop, CVPR 2019 [pdf] |
2019 |
Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G. and
Tagliasacchi, A. (2019) Cvxnet: Learnable convex decomposition Perception as Generative Reasoning Workshop, NeurIPS 2019 [pdf] |
2019 |
Kornblith, S., Norouzi, M., Lee, H. and Hinton, G. Similarity of neural network representations revisited ICML-2019 [pdf] |
2018 |
Hinton, G. E., Sabour, S. and Frosst, N. Matrix Capsules with EM Routing ICLR-2018 [pdf] |
2018 |
Kiros, J. R., Chan, W. and Hinton, G. E. Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search ACL-2018 [pdf] |
2018 |
Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G. and Hinton,
G. E. Large scale distributed neural network training through online distillation ICLR-2018 [pdf] |
2018 |
Guan, M. Y., Gulshan, V., Dai, A. M. and Hinton, G. E. Who Said What: Modeling Individual Labelers Improves Classification AAAI-2018 [pdf] |
2017 |
Sabour, S., Frosst, N. and Hinton, G. E. Dynamic Routing between Capsules NIPS-2017 [pdf] |
2017 |
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton,
G., & Dean, J. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer arXiv preprint arXiv:1701.06538 [pdf] |
2017 |
Frosst, N. and Hinton, G. E. Distilling a Neural Network Into a Soft Decision Tree arXiv preprint arXiv:1711.09784 [pdf] |
2017 |
Pereyra, G., Tucker, T., Chorowski, J., Kaiser, L. and Hinton, G. E. Regularizing neural networks by penalizing confident output distributions arXiv preprint arXiv:1701.06548 [pdf] |
2016 |
Ba, J. L., Hinton, G. E., Mnih, V., Leibo, J. Z. and Ionescu, C. Using Fast Weights to Attend to the Recent Past. NIPS-2016 arXiv preprint arXiv:1610.06258v2 [pdf] |
2016 |
Ba, J. L., Kiros, J. R. and Hinton, G. E. Layer normalization. NIPS-2016 workshop. arXiv preprint arXiv:1607.06450 [pdf] |
2016 |
Ali Eslami, S. M., Nicolas Heess, N., Theophane Weber, T., Tassa, Y., Szepesvari, D., Kavukcuoglu, K. and Hinton, G. E. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. NIPS-2016 arXiv preprint arXiv:1603.08575v3 [pdf] |
2015 |
LeCun, Y., Bengio, Y. and Hinton, G. E. Deep Learning. Nature, Vol. 521, pp 436-444. [pdf] |
2015 |
Hinton, G. E., Vinyals, O., and Dean, J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 [pdf] |
2014 |
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting The Journal of Machine Learning Research, 15(1), pp 1929-1958. [pdf] |
2014 |
Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. E. Grammar as a foreign language. arXiv preprint arXiv:1412.7449 [pdf] |
2014 |
Hinton, G. E. Where do features come from?. Cognitive Science, Vol. 38(6), pp 1078-1101. [pdf] |
2014 |
Sarikaya, R., Hinton, G. E. and Deoras, A. Application of Deep Belief Networks for Natural Language Understanding IEEE Transactions on Audio, Speech and Language Processing. [pdf] |
2014 |
Jaitly, N., Vanhoucke, V. and Hinton, G. E. Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models. Fifteenth Annual Conference of the International Speech Communication Association}. [pdf] |
2013 |
Srivastava, N., Salakhutdinov, R. R. and Hinton, G. E. Modeling Documents with a Deep Boltzmann Machine In Uncertainty in Artificial Intelligence (UAI 2013) [pdf] |
2013 |
Jaitly, N., and Hinton, G. E. Vocal Tract Length Perturbation (VTLP) improves speech recognition. Proc. ICML 2013 Workshop on Deep Learning for Audio,Speech and Language Processing}, Atlanta, USA. [pdf] |
2013 |
Graves, A., Mohamed, A. and Hinton, G. E. Speech Recognition with Deep Recurrent Neural Networks In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf] |
2013 |
Dahl, G. E., Sainath, T. N. and Hinton, G. E. Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf] |
2013 |
M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le,
P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G. Hinton On Rectified Linear Units for Speech Processing In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf] |
2013 |
Deng, L., Hinton, G. E. and Kingsbury, B. New types of deep neural network learning for speech recognition and related applications: An overview In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf] |
2013 |
Ranzato, M., Mnih, V., Susskind, J. and Hinton, G. E. Modeling Natural Images Using Gated MRFs IEEE Trans. Pattern Analysis and Machine Intelligence, Vol 35(9), pp 2206-2222. [pdf] |
2013 |
Sutskever, I., Martens, J., Dahl, G. and Hinton, G. E. On the importance of momentum and initialization in deep learning In 30th International Conference on Machine Learning, Atlanta, USA [pdf] |
2013 |
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. Tensor Analyzers In 30th International Conference on Machine Learning, Atlanta, USA [pdf] |
2012 |
Krizhevsky, A., Sutskever, I. and Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks Advances in Neural Information Processing 25, MIT Press, Cambridge, MA [pdf] |
2012 |
Salakhutdinov, R. R. and Hinton, G. E. A Better Way to Pretrain Deep Boltzmann Machines Advances in Neural Information Processing 25, MIT Press, Cambridge, MA [pdf] |
2012 |
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. and
Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors http://arxiv.org/abs/1207.0580 [pdf] |
2012 |
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed,
Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath,
and Brian Kingsbury Deep Neural Networks for Acoustic Modeling in Speech Recognition IEEE Signal Processing Magazine, November 2012 (in press) [pdf] |
2012 |
Salakhutdinov, R. R. and Hinton, G. E. An Efficient Learning Procedure for Deep Boltzmann Machines Neural Computation [pdf] |
2012 |
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. Deep Lambertian Networks International Conference on Machine Learning, [pdf] |
2012 |
Mnih, V. and Hinton, G. E. Learning to Label Aerial Images from Noisy Data International Conference on Machine Learning, [pdf] |
2012 |
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. Deep Mixtures of Factor Analysers International Conference on Machine Learning, [pdf] |
2012 |
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. Robust Boltzmann Machines for Recognition and Denoising IEEE Conference on Computer Vision and Pattern Recognition, [pdf] |
2012 |
Mohamed,A., Hinton, G. E. and Penn, G. Understanding how Deep Belief Networks perform acoustic modelling ICASSP 2012, Kyoto. [pdf] |
2012 |
van der Maaten, L., and Hinton, G. E. Visualizing non-metric similarities in multiple maps Machine Learning, Vol. 86 [pdf] |
2012 |
Mohamed, A., Dahl, G. E. and Hinton, G. E. Acoustic Modeling using Deep Belief Networks. IEEE Trans. on Audio, Speech, and Language Processing (in press) [pdf] |
2011 |
Suskever, I., Martens, J. and Hinton, G. E. Generating Text with Recurrent Neural Networks. Proc. 28th International Conference on Machine Learning, Seattle. [pdf] |
2011 |
Jaitly, N. and Hinton, G. A new way to learn acoustic events Advances in Neural Information Processing Systems 24, Deep Learning workshop. [pdf] |
2011 |
Mnih, V., Larochelle, H. and Hinton, G. Conditional Restricted Boltzmann Machines for Structured Output Prediction Proc. Uncertainty in Artificial Intelligence. [pdf] |
2011 |
Ranzato, M., Susskind, J., Mnih, V. and Hinton, G. On deep generative models with applications to recognition. IEEE Conference on Computer Vision and Pattern Recognition. [pdf] |
2011 |
Susskind,J., Memisevic, R., Hinton, G. and Pollefeys, M. Modeling the joint density of two images under a variety of transformations. IEEE Conference on Computer Vision and Pattern Recognition [pdf] |
2011 |
Hinton, G. E., Krizhevsky, A. and Wang, S. Transforming Auto-encoders., ICANN-11: International Conference on Artificial Neural Networks, Helsinki. [pdf] |
2011 |
Krizhevsky, A. and Hinton, G.E. Using Very Deep Autoencoders for Content-Based Image Retrieval. European Symposium on Artificial Neural Networks ESANN-2011, Bruges, Belgium. [pdf] |
2011 |
Jaitly, N. and Hinton, G. E. Learning a better Representation of Speech Sound Waves using Restricted Boltzmann Machines. ICASSP-2011 [pdf] |
2011 |
Mohamed,A., Sainath, T., Dahl, G. E., Ramabhadran, B., Hinton, G.
and Picheny, M. Deep Belief Networks using Discriminative Features for Phone Recognition. ICASSP-2011 [pdf] |
2011 |
Sarikaya, R. and Hinton, G. Deep Belief Nets for Natural Language Call-Routing. ICASSP-2011 [pdf] |
2011 |
Hinton, G. E. and Salakhutdinov, R. Discovering Binary Codes for Fast Document Retrieval by Learning Deep Generative Models. Topics in Cognitive Science, Vol 3, pp 74-91. [pdf coming soon] |
2010 |
Memisevic, R., Zach, C., Pollefeys, M. and Hinton, G. E. Gated Softmax Classification. Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf] |
2010 |
Ranzato, M., Mnih, V. and Hinton, G. E. Generating more realistic images using gated MRF's. Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf] |
2010 |
Dahl, G. E., Ranzato, M., Mohamed, A. and Hinton, G. E. Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf] |
2010 |
Larochelle, H. and Hinton, G. E. Learning to combine foveal glimpses with a third-order Boltzmann machine. Advances in Neural Information Processing 23, MIT Press, Cambridge, MA [pdf] |
2010 |
Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed A. and Hinton, G. Binary Coding of Speech Spectrograms Using a Deep Auto-encoder. Interspeech 2010, Makuhari, Chiba, Japan. [pdf] |
2010 |
Memisevic, R. and Hinton, G. E. Learning to represent spatial transformations with factored higher-order Boltzmann machines. Neural Computation, Vol 22, pp 1473-1492. [pdf] |
2010 |
Nair, V. and Hinton, G. E. Rectified linear units improve restricted Boltzmann machines. Proc. 27th International Conference on Machine Learning [pdf] |
2010 |
Hinton, G. E. Learning to represent visual input. Philosophical Transactions of the Royal Society, B. Vol 365, pp 177-184. [pdf] |
2010 |
Mnih, V. and Hinton, G. E. Learning to detect roads in high-resolution aerial images. European Conference on Computer Vision. [pdf] |
2010 |
Sutskever, I. and Hinton, G. E. Temporal Kernel Recurrent Neural Networks. Neural Networks, Vol 23, pp 239-243. [online text] |
2010 |
Ranzato, M. and Hinton, G. E. Modeling pixel means and covariances using factored third-order Boltzmann machines. IEEE Conference on Computer Vision and Pattern Recognition. [pdf] |
2010 |
Taylor, G., Sigal, L., Fleet, D. and Hinton, G. E. Dynamic binary latent variable models for 3D human pose tracking. IEEE Conference on Computer Vision and Pattern Recognition. [pdf] |
2010 |
Ranzato, M., Krizhevsky, A. and Hinton, G. E. Factored 3-way restricted Boltzmann machines for modeling natural images. Proc. Thirteenth International Conference on Artificial Intelligence and Statistics. [pdf] |
2010 |
Mohamed, A. and Hinton, G. E. Phone Recognition using Restricted Boltzmann Machines. To appear in ICASSP-10, Texas. [pdf] |
2009 |
Mohamed, A. R., Dahl, G. E. and Hinton, G. E. Deep belief networks for phone recognition. NIPS 22 workshop on deep learning for speech recognition. [pdf] |
2009 |
Salakhutdinov, R. and Hinton, G. E. Replicated Softmax: An Undirected Topic Model. Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.), pp 1607-1614. [pdf] |
2009 |
Nair, V. and Hinton, G. E. 3-D Object recognition with deep belief nets. Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.), pp 1339-1347. [pdf] |
2009 |
Palatucci, M, Pomerleau, D. A., Hinton, G. E. and Mitchell, T. Zero-Shot Learning with Semantic Output Codes. Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.), pp 1410-1418. [pdf] |
2009 |
Heess, N., Williams, C. K. I. and Hinton, G. E. Learning generative texture models with extended Fields-of-Experts. Proc. British Machine Vision Conf. [pdf] |
2009 |
Taylor, G. W. and Hinton, G. E. Products of Hidden Markov Models: It Takes N>1 to Tango. Proc. of the 25th Conference on Uncertainty in Artificial Intelligence. [pdf] |
2009 | Taylor, G. W. and Hinton, G. E. Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style. Proc. 26th International Conference on Machine Learning}, pp 1025-1032. Omnipress, Montreal, Quebec. [pdf] |
2009 |
Tieleman, T. and Hinton, G. E. Using Fast Weights to Improve Persistent Contrastive Divergence. Proc. 26th International Conference on Machine Learning, pp 1033-1040. Omnipress, Montreal, Quebec. [pdf] |
2009 |
Zeiler, M.D., Taylor, G.W., Troje, N.F. and Hinton, G. E. Modeling pigeon behaviour using a Conditional Restricted Boltzmann Machine. European Symposium on Artificial Neural Networks ESANN-2009. [pdf] |
2009 |
Salakhutdinov, R. and Hinton, G. E. Deep Boltzmann Machines. To appear in Artificial Intelligence and Statistics 2009 [pdf] |
2009 |
Mnih, A. and Hinton, G.~E. A Scalable Hierarchical Distributed Language Model. Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA [pdf] |
2009 |
Nair, V. and Hinton, G.~E. Implicit Mixtures of Restricted Boltzmann Machines. Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA [pdf] |
2009 |
Sutskever, I. and Hinton, G.~E. Using matrices to model symbolic relationships. Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA [pdf] |
2009 |
Sutskever, I., Hinton, G.~E. and Taylor, G. W. The Recurrent Temporal Restricted Boltzmann Machine. Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA [pdf] |
2009 |
Schmah, T., Hinton, G.~E., Zemel, R., Small, S. and Strother, S. Generative versus Discriminative Training of RBM's for classification of fMRI images. Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA [pdf] |
2008 |
van der Maaten, L. J. P. and Hinton, G. E. Visualizing Data using t-SNE. Journal of Machine Learning Research, Vol 9, (Nov) pp 2579-2605. [pdf] [supplementary material in pdf (25MB)] |
2008 |
Susskind, J.M., Hinton, G.~E., Movellan, J.R., and Anderson, A.K. Generating Facial Expressions with Deep Belief Nets. In V. Kordic (ed.) Affective Computing, Emotion Modelling, Synthesis and Recognition. ARS Publishers. [pdf] |
2008 |
Nair, V., Susskind, J., and Hinton, G.E. Analysis-by-Synthesis by Learning to Invert Generative Black Boxes. ICANN-08: International conference on Artificial Neural Networks, Prague. [pdf] |
2008 |
Yuecheng, Z., Mnih, A., and Hinton, G.~E. Improving a statistical language model by modulating the effects of context words. 16th European Symposium on Artificial Neural Networks, pages 493--498. [pdf] |
2008 |
Sutskever, I. and Hinton, G. E. Deep Narrow Sigmoid Belief Networks are Universal Approximators. Neural Computation, Vol 20, pp 2629-2636. [pdf] |
2008 |
Osindero, S. and Hinton, G.~E. Modeling image patches with a directed hierarchy of Markov random fields. Advances in Neural Information Processing Systems 20, J.C. Platt and D. Koller and Y. Singer and S. Roweis (eds.), MIT Press, Cambridge, MA [pdf] |
2008 |
Salakhutdinov, R. and Hinton, G.~E. Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. Advances in Neural Information Processing Systems 20, J.C. Platt and D. Koller and Y. Singer and S. Roweis (eds.), MIT Press, Cambridge, MA [pdf] |
2007 |
Hinton, G. E. Learning multiple layers of representation. Trends in Cognitive Sciences, Vol. 11, pp 428-434. [pdf] |
2007 |
Hinton, G.~E. To recognize shapes, first learn to generate images. In P. Cisek, T. Drew and J. Kalaska (Eds.) Computational Neuroscience: Theoretical Insights into Brain Function. Elsevier. [pdf of final draft] |
2007 |
Hinton, G.~E. (2007) Boltzmann machine. Scholarpedia, 2(5):1668 |
2007 |
Salakhutdinov R. R, Mnih, A. and Hinton, G. E. Restricted Boltzmann Machines for Collaborative Filtering International Conference on Machine Learning, Corvallis, Oregon. [pdf] |
2007 |
Mnih, A. and Hinton, G. E. Three New Graphical Models for Statistical Language Modelling International Conference on Machine Learning, Corvallis, Oregon. [pdf] |
2007 |
Salakhutdinov R. R, and Hinton, G. E. Semantic Hashing. Proceedings of the SIGIR Workshop on Information Retrieval and Applications of Graphical Models, Amsterdam. [pdf] |
2007 |
Memisevic, R. and Hinton, G. E. Unsupervised learning of image transformations. Computer Vision and Pattern Recognition (CVPR-07). [pdf A longer version: Technical Report UTML TR 2006-005. [pdf] |
2007 |
Salakhutdinov R. R, and Hinton, G. E. Learning a non-linear embedding by preserving class neighbourhood structure. AI and Statistics, 2007, Puerto Rico. [pdf] |
2007 |
Sutskever, I. and Hinton, G. E. Learning Multilevel Distributed Representations for High-dimensional Sequences. AI and Statistics, 2007, Puerto Rico. [pdf] A longer version: Technical Report UTML TR 2006-003. [pdf] |
2007 |
Cook, J. A., Sutskever, I., Mnih, A. and Hinton , G. E. Visualizing similarity data with a mixture of maps. AI and Statistics, 2007, Puerto Rico. [pdf] |
2007 |
Taylor, G. W., Hinton, G. E. and Roweis, S. Modeling human motion using binary latent variables. Advances in Neural Information Processing Systems, 19 MIT Press, Cambridge, MA [pdf] |
2006 |
Hinton, G. E. and Salakhutdinov, R. R Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006. [ abstract ] [ full paper ] [ supporting online material (pdf) ] [ Matlab code ] |
2006 |
Hinton, G. E., Osindero, S., Welling, M. and Teh, Y. Unsupervised Discovery of Non-linear Structure using Contrastive Backpropagation. Cognitive Science, 30:4, pp 725-731. [ps] [pdf] |
2006 |
Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for deep belief nets. Neural Computation 18, pp 1527-1554. [pdf] |
2006 |
Hinton, G. E. and Nair, V. Inferring motor programs from images of handwritten digits. Advances in Neural Information Processing Systems, 18, MIT Press, Cambridge, MA [ps.gz] [pdf] |
2006 |
Osindero, S., Welling, M. and Hinton, G. E. Topographic Product Models Applied To Natural Scene Statistics Neural Computation, 18, pp 381-344. [pdf] |
2005 |
Hinton, G. E., Osindero, S. and Bao, K. Learning Causally Linked Markov Random Fields. In: Artificial Intelligence and Statistics, 2005, Barbados [ps.gz] [pdf] |
2005 |
Hinton. G. E. What kind of a graphical model is the brain? International Joint Conference on Artificial Intelligence 2005, Edinburgh. [ps.gz] [pdf] |
2005 |
Mnih, A. and Hinton. G. E. Learning Unreliable Constraints using Contrastive Divergence. In IJCNN 2005, Montreal [ps.gz] [pdf] |
2005 |
Memisevic, R. and Hinton, G. E. Improving dimensionality reduction with spectral gradient descent. Neural Networks, 18, pp 702-710. [online version] |
2005 |
Carreira-Perpignan, M. A. and Hinton. G. E. On Contrastive Divergence Learning. In: Artificial Intelligence and Statistics, 2005, Barbados [pdf] |
2005 |
Goldberger, J., Roweis, S., Salakhutdinov, R and Hinton, G. E. Neighborhood Components Analysis Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, MA [pdf] |
2005 |
Memisevic, R. and Hinton, G. E. Multiple Relational Embedding Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, MA [ps.gz] [pdf] |
2005 |
Welling, M,, Rosen-Zvi, M. and Hinton, G. E. Exponential Family Harmoniums with an Application to Information Retrieval. Advances in Neural Information Processing Systems, 17, MIT Press, Cambridge, MA [ps.gz] [pdf] |
2004 |
Sallans, B and Hinton, G. E. Reinforcement Learning with Factored States and Actions. [Journal of Machine Learning Research, Vol 5 (Aug)] pp 1063--1088. |
2004 |
Bishop, C. M. Svensen, M. and Hinton, G. E. Distinguishing Text from Graphics in On-line Handwritten Ink. In Kimura, F. and Fujisawa, H. (eds.), Proceedings Ninth International Workshop on Frontiers in Handwriting Recognition, IWFHR-9, Tokyo, Japan, pp. 142-147. [pdf] |
2004 |
Hinton, G. E., Welling, M. and Mnih, A. Wormholes Improve Contrastive Divergence. Advances in Neural Information Processing Systems, 16, MIT Press, Cambridge, MA [ps.gz] [pdf] |
2004 |
Welling, M., Zemel, R. and Hinton, G. E. Probabilistic sequential independent components analysis. IEEE Transactions on Neural Networks, Vol. 15, pp 838-849. [ps.gz] [pdf] |
2003 |
Teh, Y. W, Welling, M., Osindero, S. and Hinton G. E. Energy-Based Models for Sparse Overcomplete Representations. Journal of Machine Learning Research, 4, pp 1235-1260. [ps.gz] [pdf] |
2003 |
Hinton, G. E. and Roweis, S. Stochastic Neighbor Embedding. Advances in Neural Information Processing Systems, 15, MIT Press, Cambridge, MA [ps.gz] [pdf] |
2003 |
Welling, M., Zemel, R. S., and Hinton, G. E. Efficient parametric projection pursuit density estimation. In: UAI-2003: 19th Conference on Uncertainty in Artificial Intelligence. [ps.gz] |
2003 |
Welling, M., Zemel, R. and Hinton, G. E. Self-Supervised Boosting. Advances in Neural Information Processing Systems, 15, MIT Press, Cambridge, MA [pdf] [ps.gz] |
2003 |
Welling, M., Hinton, G. E. and Osindero, S. Learning Sparse Topographic Representations with Products of Student-t Distributions. Advances in Neural Information Processing Systems, 15, MIT Press, Cambridge, MA [ps.gz] [pdf] |
2003 |
Hinton, G. E. The ups and downs of Hebb synapses. Canadian Psychology, Vol 44, pp 10-13. [pdf] |
2002 |
Hinton, G. E. (2002) Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14, pp 1771-1800. [pdf] |
2002 |
Friston, K.J., Penny, W., Phillips, C., Kiebel, S., Hinton, G. E., and
Ashburner, J. Classical and Bayesian Inference in Neuroimaging: Theory. NeuroImage, 16, pp 465-483. [pdf] |
2002 |
Brown, A. D. and Hinton, G. E. Relative Density Nets: A New Way to Combine Backpropagation with HMM's. Advances in Neural Information Processing Systems, 14, MIT Press, Cambridge, MA [pdf] [ps] [ps.gz] |
2002 |
Paccanaro, A., and Hinton, G. E. Learning Hierarchical Structures with Linear Relational Embedding. Advances in Neural Information Processing Systems, 14, MIT Press, Cambridge, MA [pdf] [ps] [ps.gz] |
2002 |
Roweis, S., Saul, L. and Hinton, G. E. Global Coordination of Local Linear Models Advances in Neural Information Processing Systems, 14, MIT Press, Cambridge, MA [.ps.gz] [pdf] |
2002 |
Oore, S., Terzopoulos, D. and Hinton, G. E. A Desktop Input Device and Interface for Interactive 3D Character Animation. Graphics Interface. [pdf] |
2002 |
Oore, S., Terzopoulos, D. and Hinton, G. E. Local Physical Models for Interactive Character Animation. Eurographics 2002, 21, Blackwell Publishers, Oxford. [pdf] |
2002 |
Welling, M. and Hinton, G. E. A New Learning Algorithm for Mean Field Boltzmann Machines. ICANN, Madrid. In Dorronsoro J.R. (ed) Lecture notes in Computer Science Vol 2415, pp 351-357, Springer. [pdf] |
2002 |
Guy Mayraz and Geoffrey Hinton (2002) Recognizing Handwritten Digits using Hierarchical Products of Experts IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, pp 189-197 [pdf] |
2001 |
Hinton G. E., Welling, M., Teh, Y. W, and Osindero, S. A New View of ICA. Proceedings of ICA-2001, San Diego, CA. [ps.gz] [pdf] |
2001 |
Hinton, G. E. and Teh, Y. W. Discovering Multiple Constraints that are Frequently Approximately Satisfied. Proceedings of Uncertainty in Artificial Intelligence (UAI-2001), pp 227-234. [ps.gz] [pdf] |
2001 |
Andrew Brown, Geoffrey Hinton Training Many Small Hidden Markov Models. Proceedings of the Workshop on Innovation in Speech Processing. [ps] [ps.gz] [pdf] |
2001 |
Andrew Brown, Geoffrey Hinton Products of Hidden Markov Models. T. Jaakkola and T. Richardson eds., Proceedings of Artificial Intelligence and Statistics 2001, Morgan Kaufmann, pp 3-11 [abstract] [ps] [ps.gz] [pdf] |
2001 |
Yee-Whye Teh, Geoffrey Hinton Rate-coded Restricted Boltzmann Machines for Face Recognition Advances in Neural Information Processing Systems 13, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf] |
2001 |
Brian Sallans, Geoffrey Hinton Using Free Energies to Represent Q-values in a Multiagent Reinforcement learning Task Advances in Neural Information Processing Systems 13, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf] |
2001 |
Guy Mayraz, Geoffrey Hinton Recognizing Hand-Written Digits Using Hierarchical Products of Experts Advances in Neural Information Processing Systems 13, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf] |
2000 |
Hinton, G.E. Training Products of Experts by Minimizing Contrastive Divergence Technical Report: GCNU TR 2000-004 [abstract] [ps.gz] [pdf] |
2000 |
Paccanaro, A. and Hinton, G.E Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions Proceedings of the International Joint Conference on Neural Networks, IJCNN 2000 [pdf] [ps] [ps.gz] |
2000 |
Hinton, G.E. Modelling High-Dimensional Data by Combining Simple Experts. AAAI-2000: Seventeenth National Conference on Artificial Intelligence, Austin, Texas. [pdf] |
2000 |
Paccanaro, A., and Hinton, G.E. Learning Distributed Representations of Concepts from Relational Data Using Linear Relational Embedding IEEE Transactions on Knowledge and Data Engineering, 13, pp 232-245. (Online preprint version is Technical Report: GCNU TR 2000-002) [abstract] [ps.gz]] [pdf] |
2000 |
Hinton, G.E. , and Brown, A Spiking Boltzmann machines. In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf] |
2000 |
Hinton, G.E. , Ghahramani, Z and Teh Y. W. Learning to parse images. In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA [abstract] [ps.gz] [pdf] |
2000 |
Ghahramani, Z. and Hinton, GE Variational learning for switching state-space models. [abstract] [ps.gz] [pdf] Neural Computation, 12, pp 831-864, |
2000 |
Ueda, N. Nakano, R., Ghahramani, Z and Hinton, G.E. SMEM algorithm for mixture models. [abstract] [ps.gz] [pdf] Neural Computation, 12, pp 2109-2128 |
2000 |
Paccanaro, A., and Hinton, G.E. Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space ICML-2000, Proceedings of the Seventeenth International Conference on Machine Learning, Langley P. (Ed.), 711-718, Stanford University, Morgan Kaufmann Publishers, San Francisco. [ps]] [ps.gz]] [pdf] |
1999 |
Hinton, G.E. Supervised learning in multilayer neural networks in The MIT Encyclopedia of the Cognitive Sciences Editors: Robert A. Wilson and Frank C. Keil The MIT Press. [ps] [pdf] |
1999 |
Hinton, G.E Training products of experts by maximizing contrastive likelihood Technical Report: GCNU TR1999-001 [ See updated version above, GCNU TR 2000-004] |
1999 |
Hinton, G.E. Products of experts Proceedings of the Ninth International Conference on Artificial Neural Networks [ICANN 99 Vol 1 pages 1-6]. [abstract] [ps] [pdf] |
1999 |
Ghahramani, Z., Korenberg, A.T. and Hinton, G.E. Scaling in a hierarchical unsupervised network. [abstract] [ps.gz] [pdf] Proceedings of the Ninth International Conference on Artificial Neural Networks [ICANN 99, Vol 1 pages 13-18] |
1999 |
Hinton, G.E., and T. Sejnowski (eds) Unsupervised Learning: Foundations of Neural Computation June 1999, MIT Press |
1999 |
Frey, B.J., Hinton, G.E. Variational learning in nonlinear gaussian belief networks [abstract] [ps] [pdf] Neural computation 11:1, 193-214 |
1998 |
Neal, R.M. and Hinton, G.E. A view of the EM algorithm that justifies incremental, sparse, and other variants [abstract] [ps] [pdf] in Learning in Graphical Model M.I. Jordan (editor) |
1998 |
de Sa, V.R. and Hinton, G.E. Cascaded redundancy reduction. [abstract] [ps] [pdf] Network: Computation in Neural Systems, 9, 73-84 |
1998 |
Tibshirani, R. and Hinton, G.E. Coaching variables for regression and classification [abstract] [ps] [pdf] Statistics and Computing, 8, 25-33 |
1998 |
Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R. A comparison of statistical learning methods on the GUSTO database. Statistics in Medicine, 17, pp 2501-2508. [pdf] |
1998 |
Ghahramani, Z. and Hinton, G.E Hierarchical nonlinear factor analysis and topographic maps [abstract] [ps.gz] [pdf] Advances in Neural Information Processing Systems 10. MIT Press: Cambridge, MA. |
1998 |
Grzeszczuk, R., Terzopoulos, D., and Hinton, G.~E. NeuroAnimator: Fast Neural Network Emulation and Control of Physics-Based Models. Proc. ACM SIGGRAPH-98, Computer Graphics Proceedings, Annual Conference Series, pp 9-20. [pdf] |
1998 |
Fels, S. S. and Hinton, G. E. Glove-TalkII: A neural network interface which maps gestures to parallel formant speech synthesizer controls. [abstract] [ps] [pdf] IEEE Transactions on Neural Networks, Vol 9 No 1, 205-212 |
1997 |
Hinton, G.E. , Sallans, B., and Ghahramani, Z. A hierarchical community of experts [abstract] [ps.gz] [pdf] Learning in Graphical Model, 479-494, Kluwer Academic Publishers |
1997 |
Williams, C. K. I., Revow, M. and Hinton, G. E. Instantiating deformable models with a neural net [abstract] [ps] [pdf] Computer Vision and Image Understanding, Vol. 68, No. 1, Oct 1997, pp. 120-126 |
1997 |
Hinton, G.E., Dayan, P. and Revow, M. Modeling the manifolds of images of handwritten digits. IEEE Transactions on Neural Networks, 8 65-74 [abstract] [ps] [ps.gz] [pdf] |
1997 |
Hinton, G.E. and Revow, M. Using mixtures of factor analyzers for segmentation and pose estimation [abstract] [ps] [ps.gz] [pdf] |
1997 |
Hinton, G. E. and Ghahramani, Z. Generative models for discovering sparse distributed representations [abstract] [ps] [pdf] Philosophical Transactions of the Royal Society of London, B, 352: 1177-1190 |
1997 |
Hinton, G.E. and Zemel, R.S. Minimizing description length in an unsupervised neural network [abstract] [ps] [pdf] |
1997 |
Dayan, P. and Hinton, G. E. Using EM for reinforcement learning [abstract] [ps.gz] [pdf] Neural Computation, 9, 271-278 |
1997 |
Oore, S, Hinton, G.E., and Dudek G. A mobile robot that learns its place [abstract] [ps] [pdf] Neural Computation 9:3 683-699 |
1997 |
Frey, B.J. Hinton, G.E. Efficient stochastic source coding and an application to a bayesian network source model The computer Journal, 40, No. 2/3, 157-165. [abstract] [ps] [pdf] |
1997 |
Bishop, C. M., Hinton, G.~E. and Strachan, I. D. G. GTM through time. Proceedings IEE Fifth International Conference on Artificial Neural Networks}. pp 111--116. IEE, London. [pdf] |
1996 |
Frey, B. J., Hinton, G. E. and Dayan, P. Does the wake-sleep algorithm learn good density estimators? [abstract] [ps] [ps.gz] [pdf] Advances in Neural Information Processing Systems 8. MIT Press, Cambridge, MA. |
1996 |
Frey, B. J. and Hinton, G. E. A simple algorithm that discovers efficient perceptual codes [abstract] [ps] [pdf] L. Harris and M. Jenkin (Eds) Computational and Biological Mechanisms of Visual Coding, Cambridge University press, New York. |
1996 |
Dayan, P. and Hinton, G. E. Varieties of Helmholtz machines. Neural Networks, 9 1385-1403. [abstract] [pdf] |
1996 |
Hinton, G. E. and Revow, M. Using pairs of data-points to define splits for decision trees [abstract] [ps] [pdf] Advances in Neural Information Processing Systems 8. D.S. Touretzky, M.C. Mozer and M.E. Hasselmo. MIT Press. |
1996 |
Ghahramani, Z. and Hinton, G. E. The EM algorithm for mixtures of factor analyzers. [abstract] [ps.gz] [pdf] Technical Report CRG-TR-96-1, University of Toronto. |
1996 |
Ghahramani, Z. and Hinton, G. E. Parameter estimation for linear dynamical systems. [abstract] [ps.gz] [pdf] Technical Report CRG-TR-96-2, University of Toronto. |
1996 |
Frey, B. J., and Hinton, G.~E. Free energy coding. Proceedings of the Data Compression Conference 1996, IEEE Computer Society Press, Los Alamitos, CA. |
1996 |
Revow, M., Williams, C. K. I. and Hinton, G. E. Using Generative Models for Handwritten Digit Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence,18, 592-606. [abstract] [ps] [pdf] |
1995 |
Hinton, G. E., Revow, M. and Dayan P. Recognizing handwritten digits using mixtures of linear models. [abstract] [ps] [ps.gz] [pdf] Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 1015-1022 MIT Press, Cambridge MA. |
1995 |
Williams, C. K. I., Hinton, G. E. and Revow, M. Using a neural net to instantiate a deformable model. [abstract] [ps] [pdf] Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 965-972 MIT Press, Cambridge MA. |
1995 |
Xu, L., Jordan, M. I. and Hinton, G.~E. An alternative model for mixtures of experts. Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 633-640 MIT Press, Cambridge MA. [pdf] |
1995 |
Fels, S. S. and Hinton, G.~E. GloveTalkII: Mapping hand gestures to speech using neural networks. Advances in Neural Information Processing Systems 7. G. Tesauro, D. S. Touretzky and T. K. Leen (Eds), pp 843-850 MIT Press, Cambridge MA. [pdf] |
1995 |
Zemel, R. S. and Hinton, G. E. Learning population codes by minimizing description length. [abstract] [ps.gz] [pdf] Neural Computation, 7, 549-564. |
1995 |
Hinton, G. E., Dayan, P., Frey, B. J. and Neal, R. The wake-sleep algorithm for unsupervised Neural Networks. [abstract] [ps] [pdf] Science, 268, 1158-1161. |
1995 |
Dayan, P., Hinton, G. E., Neal, R., and Zemel, R. S. The Helmholtz machine [abstract] [ps.gz] [pdf] Neural Computation, 7, 1022-1037. |
1995 |
Hinton, G. E., Dayan, P., To, A. and Neal R. M. The Helmholtz machine through time. [abstract] [ps] [pdf] F. Fogelman-Soulie and R. Gallinari (editors) ICANN-95, 483-490 |
1995 |
Hinton, G. E. and Frey, B. J. Using neural networks to monitor for rare failures. Proceedings of the 37th Mechanical Working and Steel Processing Conference, Hamilton, Ontario. [pdf] |
1994 |
Hinton, G. E. and Zemel, R. S. Autoencoders, minimum description length, and Helmholtz free energy. [abstract] [ps] [pdf] Advances in Neural Information Processing Systems 6. J. D. Cowan, G. Tesauro and J. Alspector (Eds.), Morgan Kaufmann: San Mateo, CA. |
1994 |
Zemel, R.S. and Hinton, G. E. Developing population codes by minimizing description length. [abstract] [ps] [pdf] Advances in Neural Information Processing Systems 6. J. D. Cowan, G. Tesauro and J. Alspector (Eds.), Morgan Kaufmann: San Mateo, CA. |
1994 |
Xu, L. Jordan, M. I. and Hinton, G.~E. A modified gating network for the mixtures of experts architectures. Proc. WCNN-94, San Diego, CA. vol. 2, pp. 405-410. [pdf] |
1993 |
Williams, C. K. I., Revow, M. and Hinton, G. E. Hand-printed digit recognition using deformable models. L. Harris and M. Jenkin (Eds) Spatial Vision in Humans and Robots Cambridge University press, New York. |
1993 |
Revow, M., Williams, C.K.I, and Hinton, G.E. Using mixtures of deformable models to capture variations in the shapes of hand-printed digits. [abstract] [ps] [pdf] Third International Workshop on Frontiers of Handwriting Recognition. |
1993 |
Hinton, G. E. and van Camp, D. Keeping neural networks simple by minimizing the description length of the weights [abstract] [ps] [pdf] Sixth ACM Conference on Computational Learning Theory, Santa Cruz, July 1993. |
1993 |
Hinton, G. E., Plaut, D. C. and Shallice, T. Simulating brain damage Scientific American, October Issue [pdf] |
1993 |
Becker, S. and Hinton, G. E. Learning mixture models of spatial coherence Neural Computation 5, 267-277. [abstract] [pdf] |
1993 |
Nowlan. S. J. and Hinton, G. E. A soft decision-directed LMS algorithm for blind equalization. IEEE Transactions on Communications, 41, 275-279. [pdf] |
1993 |
Dayan, P. and Hinton, G. E. Feudal reinforcement learning. [abstract] [ps.gz] [pdf] Advances in Neural Information Processing Systems 5. S. J. Hanson, J. D. Cowan and C. L. Giles (Eds.), Morgan Kaufmann: San Mateo, CA. |
1992 |
Fels, S. S. and Hinton, G. E. Glove-Talk: A neural network interface between a data-glove and a speech synthesizer. [abstract] [ps] [pdf] IEEE Transactions on Neural Networks, 3, No 6 |
1992 |
Hinton, G. E., Williams, C. K. I., and Revow, M. Combining two methods of recognizing hand-printed digits. Artificial Neural Networks II: Proceedings of ICANN-92. I. Aleksander and J. Taylor (Eds.), Elsevier North-Holland. [pdf] |
1992 |
Hinton, G. E., Williams, C. K. I., and Revow, M. Adaptive elastic models for character recognition. [abstract] [ps.gz] [pdf] Advances in Neural Information Processing Systems 4. J. E. Moody, S. J. Hanson and R. P. Lippmann (Eds.), Morgan Kaufmann: San Mateo, CA. |
1992 |
Nowlan. S. J. and Hinton, G. E. Simplifying neural networks by soft weight sharing. [pdf] Neural Computation, 4, 173-193. |
1992 |
Becker, S. and Hinton, G. E. A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355:6356, 161-163 [abstract] [pdf] [Commentary by Graeme Mitchison and Richard Durbin in the News and Views section of Nature] |
1992 |
Hinton, G.E. How neural networks learn from experience. Scientific American, September 1992. [pdf] |
1991 |
Jacobs, R., Jordan, M. I., Nowlan. S. J. and Hinton, G. E. Adaptive mixtures of local experts. [abstract] [ps] [pdf] Neural Computation, 3, 79-87. |
1991 |
Nowlan, S. J. and Hinton, G. E. Evaluation of adaptive mixtures of competing experts. [abstract] [ps] [pdf] Advances in Neural Information Processing Systems 3. R. P. Lippmann, J. E. Moody, and D. S. Touretzky (Eds.), Morgan Kaufmann: San Mateo, CA. |
1991 |
Zemel, R.S. Hinton, G.E. Discovering viewpoint-invariant relationships that characterize objects Advances in Neural Information Processing Systems 3. R. P. Lippmann, J. E. Moody, and D. S. Touretzky (Eds.), Morgan Kaufmann: San Mateo, CA. [pdf] |
1991 |
Hinton, G. E. and Shallice, T. Lesioning an attractor network: Investigations of acquired dyslexia. Psychological Review, 98, 74-95. [pdf] |
1990 |
Williams, C. K. I. and Hinton, G. E. Mean field networks that learn to discriminate temporally distorted strings. Touretzky, D. S., Elman, J. L., Sejnowski, T. J. and Hinton, G. E. (Eds.) Connectionist Models: Proceedings of the 1990 Connectionist Summer School. Morgan Kauffman: San Mateo, CA. [abstract] [ps] [pdf] |
1990 |
Zemel, R.S. Mozer, M.C., Hinton, G.E. Traffic: Recognizing objects using hierarchical reference frame transformations Touretzky, D. S., (Ed.) Advances in Neural Information Processing Systems 2, Morgan Kaufmann: San Mateo, CA. [pdf] |
1990 |
Galland, C. G. and Hinton, G. E. Discovering higher-order features with mean field networks. Touretzky, D. S., (Ed.) Advances in Neural Information Processing Systems 2, Morgan Kaufmann: San Mateo, CA. [pdf] |
1990 |
Lang, K. J. and Hinton, G. E. Dimensionality reduction and prior knowledge in E-set recognition. Touretzky, D. S., (Ed.) Advances in Neural Information Processing Systems 2, Morgan Kaufmann: San Mateo, CA. [pdf] |
1990 |
Galland, C. G. and Hinton, G. E. Deterministic Boltzmann learning in networks with asymmetric connectivity. Touretzky, D. S., Elman, J. L., Sejnowski, T. J. and Hinton, G. E. (Eds.) Connectionist Models: Proceedings of the 1990 Connectionist Summer School. Morgan Kauffman: San Mateo, CA. |
1990 |
Hinton, G.E. Preface to the special issue on connectionist symbol processing Artificial Intelligence 46, 1-4. [ps] [pdf] |
1990 |
Lang, K., Waibel, A. and Hinton, G. E. A time-delay neural network architecture for isolated word recognition. Neural Networks, 3, 23-43.[pdf] |
1990 |
Hinton, G. E. Mapping part-whole hierarchies into connectionist networks. Artificial Intelligence, 46, 47-75. [pdf] |
1990 |
Hinton, G.E., Becker, S. An unsupervised learning procedure that discovers surfaces in random-dot stereograms Proc. International Joint Conference on Neural Networks, Washington, DC, 1990 [pdf] |
1990 |
Hinton, G. E. and Nowlan, S. J. The bootstrap Widrow-Hoff rule as a cluster-formation algorithm. Neural Computation, 2, 355-362. [pdf] |
1989 |
Hinton, G. E. Deterministic Boltzmann learning performs steepest descent in weight-space. Neural Computation, 1, 143-150. [pdf] |
1989 |
Waibel, A. Hanazawa, T. Hinton, G. Shikano, K. and Lang, K. Phoneme recognition using time-delay neural networks. IEEE Acoustics Speech and Signal Processing, 37, 328-339. [pdf] |
1989 |
LeCun, Y., Galland, C. C., and Hinton, G. E. GEMINI: Gradient Estimation by Matrix Inversion after Noise Injection. Touretzky, D. S., (Ed.) Neural Information Processing Systems 1, Morgan Kaufmann: San Mateo, CA. [pdf] |
1989 |
Hinton, G.E. Connectionist learning procedures. Artificial Intelligence 40, 185-234. [pdf] |
1988 |
Touretzky, D. S. and Hinton, G. E. A distributed connectionist production system. Cognitive Science, 12, 423-466. [pdf] |
1988 |
Hinton, G. E. and Parsons, L. A. Scene-based and viewer-centered representations for comparing shapes. Cognition, 30, 1--35.[pdf] |
1988 |
Hinton, G. E. and McClelland, J. L. Learning representations by recirculation. In D. Z. Anderson, editor, Neural Information Processing Systems, pages 358--366, American Institute of Physics: New York. [pdf] |
1988 |
Hinton, G. E. Representing part-whole hierarchies in connectionist networks. Proceedings of the Tenth Annual Conference of the Cognitive Science Society. Montreal, Canada. [pdf] |
1987 |
Fahlman, S. E. and Hinton, G. E. Connectionist architectures for Artificial Intelligence. IEEE Computer, 20, 100--109. [pdf] |
1987 |
Hinton, G. E. Learning translation invariant recognition in a massively parallel network. In Goos, G. and Hartmanis, J., editors, PARLE: Parallel Architectures and Languages Europe, pages 1--13, Lecture Notes in Computer Science, Springer-Verlag, Berlin. [pdf] |
1987 |
Hinton, G.E. The horizontal-vertical delusion Perception, 16, 5, 677-680 [pdf] |
1987 |
Hinton, G. E. and Plaut, D. C. Using fast weights to deblur old memories. Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA [pdf] |
1987 |
Hinton, G. E. and Nowlan, S. J. How learning can guide evolution. Complex Systems, 1, 495--502. [ pdf ] This paper was rejected by the Cognitive Science Society Conference (against the advice of the referees) because the chairman of the organizing committee thought it might mislead cognitive scientists. [Commentary by John Maynard Smith in the News and Views section of Nature] |
1987 |
Plaut, D.C. and Hinton, G.E. Learning sets of filters using back-propagation Computer Speech and Language, 2, 35-61 [ pdf ] |
1987 |
Sejnowski, T.~J. and Hinton, G.~E. Separating figure from ground using a Boltzmann machine. In Arbib, M. and Hanson, A.~R., editors, Vision, Brain and Cooperative Computation, MIT Press, Cambridge, MA. [pdf] |
1986 |
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning representations by back-propagating errors. Nature, 323, 533--536.[pdf] [Commentary from News and Views section of Nature] |
1986 |
Kienker, P. K., Sejnowski, T. J., Hinton, G. E., and Schumacher, L. E. Separating figure from ground with a parallel network. Perception, 15, 197--216. [pdf] |
1986 |
Sejnowski, T. J., Kienker, P. K., and Hinton, G. E. Learning symmetry groups with hidden units: Beyond the perception. .Physica D, 22, 260--275. [pdf] |
1986 |
Hinton, G. E. Learning distributed representations of concepts. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, Mass. [ pdf ] Reprinted in Morris, R. G. M. editor, Parallel Distributed Processing: Implications for Psychology and Neurobiology, Oxford University Press, Oxford, UK |
1986 |
Plaut, D., Nowlan, S. and Hinton, G. E. Experiments on learning by back-propagation Technical Report CMU-CS-86-126. Department of Computer Science, Carnegie-Mellon University. [ pdf ] |
1986 |
McClelland, J. L., Rumelhart, D. E., and Hinton, G. E. The appeal of for Parallel Distributed Processing In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 3-44. [pdf] |
1986 |
Rumelhart, D. E., Hinton, G. E., and McClelland, J. L. A general framework for Parallel Distributed Processing In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 45-76. [pdf] |
1986 |
Hinton, G. E., McClelland, J. L., and Rumelhart, D. E. Distributed representations. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 77-109. [pdf] |
1986 |
Hinton, G. E. and Sejnowski, T. J. Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 282-317. [pdf] |
1986 |
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning internal representations by error propagation. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 318-362. [pdf] |
1986 |
Rumelhart, D. E., Smolensky, P., McClelland, J. L., and Hinton, G.
E. Parallel distributed models of schemata and sequential thought processes. In McClelland, J. L. and Rumelhart, D. E., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models, MIT Press, Cambridge, MA. pp 7-57. [pdf] |
1986 |
Pearlmutter, B. A. and Hinton, G. E. G-maximization: An unsupervised learning procedure for discovering regularities. In Denker, J., editor, Neural Networks for Computing: American Institute of Physics Conference Proceedings, Vol 151, pp 333-338. [pdf] |
1985 |
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147-169. [pdf] |
1985 |
Touretzky, D. S. and Hinton, G. E. Symbols among the neurons: Details of a connectionist inference architecture. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles. [pdf] |
1985 |
Hinton, G.E. and Lang, K.J. Shape recognition and illusory conjunctions Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, pp 252-259 [pdf] |
1985 |
Hinton, G.E. Learning in parallel networks. Byte, April issue. [pdf] |
1985 |
Szeliski, R. and Hinton, G. E. Solving random-dot stereograms using the heat equation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, San Francisco. |
1984 |
Hinton, G. E. Parallel computations for controlling an arm. The Journal of Motor Behavior, 16, 171-194. [pdf] |
1984 |
Hinton, G.~E., Sejnowski, T. J., and Ackley, D. H. Boltzmann Machines: Constraint satisfaction networks that learn. Technical Report CMU-CS-84-119, Carnegie-Mellon University. [pdf] |
1984 |
Hutchins, E. L. and Hinton, G. E. Why the islands move. Perception, 13, 629--632. [pdf] |
1984 |
Hammond, N., Hinton, G.E., Barnard, P., Long, J. and Whitefield, A Evaluating the interface of a document processor: A comparison of expert judgement and user observation. Proceedings of the First IFIP Conference on Human-Comuter Interaction, North-Holland |
1984 |
Hinton, G. E. Some computational solutions to Bernstein's problems. In Whiting, H., editor, Human Motor Actions: Bernstein Reassessed, North-Holland, New York. [pdf] |
1983 |
Ballard, D. H., Hinton, G. E., and Sejnowski, T. J. Parallel visual computation. Nature, 306, 21--26.[pdf] |
1983 |
Hinton, G. E. and Sejnowski, T. J. Optimal perceptual inference. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Washington DC. [pdf] |
1983 |
Fahlman, S.E., Hinton, G.E. and Sejnowski, T.J. Massively parallel architectures for A.I.: Netl, Thistle, and Boltzmann machines. Proceedings of the National Conference on Artificial Intelligence, Washington DC. [pdf] |
1983 |
Hinton, G.E. and Sejnowski, T.J. Analyzing cooperative computation. Proceedings of the Fifth Annual Conference of the Cognitive Science Society, Rochester NY. [pdf] |
1981 |
Hinton, G.E. The role of spatial working memory in shape perception. Proceedings of the Third Annual Conference of the Cognitive Science Society, Berkeley CA. [pdf] |
1981 |
Hinton, G. and Anderson, J. Parallel models of associative memory. Lawrence Erlbaum Assoc., Hillsdale, NJ. |
1981 |
Anderson, J. A. and Hinton, G. E. Models of information processing in the brain. In Hinton, G. E. and Anderson, J. A, editors, Parallel Models of Associative Memory, Erlbaum, Hillsdale, NJ. [pdf] |
1981 |
Hinton, G. E. Implementing semantic networks in parallel hardware. In Hinton, G. E. and Anderson, J. A., editors, Parallel Models of Associative Memory, Erlbaum, Hillsdale, NJ. [pdf] |
1981 |
Hinton, G.E. A parallel computation that assigns canonical object-based frames of reference. Proceedings of the Seventh International Joint Conference on Artificial Intelligence Vol 2, Vancouver BC, Canada [pdf] |
1981 |
Hinton, G. E. and Parsons, L. A. Frames of reference and mental imagery. In Long, J. and Baddeley, A., editors, Attention and Performance IX, Erlbaum, Hillsdale, NJ. [pdf] |
1981 |
Hinton, G.E. Shape representation in parallel systems Proceedings of the Seventh International Joint Conference on Artificial Intelligence Vol 2, Vancouver BC, Canada [pdf] |
1979 |
Hinton, G. E. Some demonstrations of the effects of structural descriptions in mental imagery. Cognitive Science, 3, 231-250. [pdf] |
1979 |
Hinton, G. E. Imagery without Arrays. Behavioral and Brain Sciences, 2, 555-556. [pdf] |
1978 |
Sloman, A., Owen, D. Hinton., G., Birch, F. and O'Gorman, F. Representation and control in vision. Proceedings of the A.I.S.B. Summer Conference, Hamburg |
1978 |
Hinton, G.E. Respectively reconsidered Pragmatics Microfiche, May issue |
1978 |
Hinton, G. E. Relaxation and its role in vision. PhD Thesis, University of Edinburgh. |
1976 |
Hinton, G.E. Using relaxation to find a puppet Proceeding of the A.I.S.B. Summer Conference, University of Edinburgh [pdf] |
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