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 until
September 2013.
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]
A practical guide to training restricted Boltzmann machines
[pdf]
Recent Papers
Mohamed, A., Dahl, G. E. and Hinton, G. E. (2012)
Acoustic Modeling using Deep Belief Networks.
IEEE Trans. on Audio, Speech, and Language Processing (in press)
[pdf]
van der Maaten, L., and Hinton, G. E. (2012)
Visualizing non-metric similarities in multiple maps.
Machine Learning, Vol. 86
[pdf]
Suskever, I., Martens, J. and Hinton, G. E. (2011)
Generating Text with Recurrent Neural Networks.
Proc. 28th International Conference on Machine Learning, Seattle.
[pdf]
Taylor, G. W, Hinton, G. E., and Roweis, S. (2011)
Two distributed-state models for generating high-dimensional time series.
Journal of Machine Learning Research, vol 12, pp 863-907.
[pdf]
Ranzato, M., Susskind, J., Mnih, V. and Hinton, G. (2011)
On deep generative models with applications to recognition.
IEEE Conference on Computer Vision and Pattern Recognition
[pdf]
Mnih, V., Larochelle, H. and Hinton, G. (2011)
Conditional Restricted Boltzmann Machines for Structured Output Prediction
Uncertainty in Artificial Intelligence.
[pdf]
Susskind,J., Memisevic, R., Hinton, G. and Pollefeys, M. (2011)
Modeling the joint density of two images under a variety of transformations.
IEEE Conference on Computer Vision and Pattern Recognition
[pdf]
Hinton, G. E., Krizhevsky, A. and Wang, S. (2011)
Transforming Auto-encoders.
ICANN-11: International Conference on Artificial Neural Networks, Helsinki.
[pdf]
Krizhevsky, A. and Hinton, G.E. (2011)
Using Very Deep Autoencoders for Content-Based Image Retrieval.
European Symposium on Artificial Neural Networks ESANN-2011, Bruges, Belgium.
[pdf]
Jaitly, N. and Hinton, G. E. (2011)
Learning a better Representation of Speech Sound Waves using
Restricted Boltzmann Machines.
ICASSP-2011
[pdf]
Mohamed,A., Sainath, T., Dahl, G. E., Ramabhadran, B., Hinton, G.
and Picheny, M. (2011)
Deep Belief Networks using Discriminative Features for Phone
Recognition.
ICASSP-2011
[pdf]
Memisevic, R., Zach, C., Pollefeys, M. and Hinton, G. E. (2010)
Gated Softmax Classification.
Advances in Neural Information Processing 23.
[pdf]
Ranzato, M., Mnih, V. and Hinton, G. E. (2010)
Generating more realistic images using gated MRF's.
Advances in Neural Information Processing 23.
[pdf]
Dahl, G. E., Ranzato, M., Mohamed, A. and Hinton, G. E. (2010)
Phone Recognition with the Mean-Covariance Restricted Boltzmann
Machine.
Advances in Neural Information Processing 23.
[pdf]
Larochelle, H. and Hinton, G. E. (2010)
Learning to combine foveal glimpses with a third-order Boltzmann machine.
Advances in Neural Information Processing 23.
[pdf]
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]
