Geoffrey E. Hinton
| Department of Computer Science | email: hinton [at] cs [dot] toronto [dot] edu | |||
| University of Toronto | voice: 416-978-7564 | |||
| 6 King's College Rd. | fax: 416-978-1455 | |||
| Toronto, Ontario | office: Pratt 290G | |||
| M5S 3G4, CANADA | Directions for Visitors | |||
Check out the new web page for
Machine Learning at Toronto
Information for prospective students:
I will not be taking any more graduate students or postdocs until
September 2011.
Basic papers on deep learning
Hinton, G. E., Osindero, S. and Teh, Y. (2006)
A fast learning algorithm for deep belief nets.
Neural Computation, 18, pp 1527-1554.
[pdf]
Movies of the neural network generating and recognizing digits
Hinton, G. E. and Salakhutdinov, R. R. (2006)
Reducing the dimensionality of data with neural networks.
Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
[
full paper ]
[
supporting online material (pdf) ]
[
Matlab code ]
Papers on deep learning without much math
Hinton, G. E. (2007)
Learning Multiple Layers of Representation.
Trends in Cognitive Sciences, Vol. 11, pp 428-434.
[pdf]
Hinton, G. E. (2007)
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]
Recent Papers
Memisevic, R. and Hinton, G. E. (2010)
Learning to represent spatial transformations with factored
higher-order Boltzmann machines.
Neural Computation, Vol 22, pp 1473-1492.
[pdf]
Nair, V. and Hinton, G. E. (2010)
Rectified linear units improve restricted Boltzmann machines.
Proc. 27th International Conference on Machine Learning
[pdf]
Hinton, G. E. (2010)
Learning to represent visual input.
Philosophical Transactions of the Royal Society, B.
Vol 365, pp 177-184.
[pdf]
Mnih, V. and Hinton, G. E. (2010)
Learning to detect roads in high-resolution aerial images.
European Conference on Computer Vision.
[pdf]
Sutskever, I. and Hinton, G. E. (2010)
Temporal Kernel Recurrent Neural Networks.
Neural Networks, Vol 23, pp 239-243.
[online text]
Ranzato, M. and Hinton, G. E. (2010)
Modeling pixel means and covariances using factored third-order
Boltzmann machines.
IEEE Conference on Computer Vision and Pattern Recognition.
[pdf]
Taylor, G., Sigal, L., Fleet, D. and Hinton, G. E. (2010)
Dynamic binary latent variable models for 3D human pose tracking.
IEEE Conference on Computer Vision and Pattern Recognition.
[pdf]
Ranzato, M., Krizhevsky, A. and Hinton, G. E. (2010)
Factored 3-way restricted Boltzmann machines for modeling natural
images.
Proc. Thirteenth International Conference on Artificial Intelligence and Statistics.
[pdf]
Mohamed, A. R., Dahl, G. and Hinton, G. E. (2009)
Deep belief networks for phone recognition.
NIPS 22 workshop on deep learning for speech recognition.
[pdf]
Salakhutdinov, R. and Hinton, G. E. (2009)
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]
Nair, V. and Hinton, G. E. (2009)
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]
Palatucci, M, Pomerleau, D. A., Hinton, G. E. and Mitchell, T. (2009)
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]
Heess, N., Williams, C. K. I. and Hinton, G. E. (2009)
Learning generative texture models with extended Fields-of-Experts.
Proc. British Machine Vision Conf.
[pdf]
Taylor, G. W. and Hinton, G. E. (2009)
Products of Hidden Markov Models: It Takes N>1 to Tango.
Proc. of the 25th Conference on Uncertainty in Artificial Intelligence.
[pdf]
Factored Conditional Restricted Boltzmann Machines for
Modeling Motion Style.
Proc. 26th International Conference on Machine Learning},
pp 1025-1032. Omnipress, Montreal, Quebec.
[pdf]
Tieleman, T. and Hinton, G. E. (2009)
Using Fast Weights to Improve Persistent Contrastive Divergence.
Proc. 26th International Conference on Machine Learning,
pp 1033-1040. Omnipress, Montreal, Quebec.
[pdf]
Salakhutdinov, R. and Hinton, G.~E. (2009)
Deep Boltzmann Machines.
Artificial Intelligence and Statistics.
[pdf]
Mnih, A. and Hinton, G.~E. (2009)
A Scalable Hierarchical Distributed Language Model.
Advances in Neural Information Processing Systems 21,
MIT Press, Cambridge, MA.
[pdf]
