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, visiting students,
summer students or visitors, so please do not apply to work with me.
News
Results of the 2012 competition to recognize 1000 different types of object
How George Dahl won the competition to predict the activity of potential drugs
How Vlad Mnih won the competition to predict job salaries from job advertisements
How Laurens van der Maaten won the competition to visualize a dataset of potential drugs
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)
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]
Hinton, G. E. (2007)
Learning Multiple Layers of Representation.
Trends in Cognitive Sciences, Vol. 11, pp 428-434.
[pdf]
A practical guide to training restricted Boltzmann machines
[pdf]
Recent Papers
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. and
Salakhutdinov, R. R. (2012)
Improving neural networks by preventing co-adaptation of feature detectors
http://arxiv.org/abs/1207.0580
[pdf]
G. Hinton, L. Deng, D. Yu, G. Dahl, A.Mohamed, N. Jaitly,
A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury
Deep Neural Networks for Acoustic Modeling in Speech Recognition
IEEE Signal Processing Magazine, 29, November 2012 (in press)
[pdf]
Salakhutdinov, R. R. and Hinton, G. E. (2012)
An Efficient Learning Procedure for Deep Boltzmann Machines.
Neural Computation
[pdf]
Mohamed, A., Dahl, G. E. and Hinton, G. E. (2012)
Acoustic Modeling using Deep Belief Networks.
IEEE Trans. on Audio, Speech, and Language Processing, 20, pp 14-22.
[pdf]
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2012)
Deep Lambertian Networks.
International Conference on Machine Learning,
[pdf]
Mnih, V. and Hinton, G. E. (2012)
Learning to Label Aerial Images from Noisy Data.
International Conference on Machine Learning,
[pdf]
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2012)
Deep Mixtures of Factor Analysers.
International Conference on Machine Learning,
[pdf]
Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2012)
Robust Boltzmann Machines for Recognition and Denoising.
IEEE Conference on Computer Vision and Pattern Recognition,
[pdf]
Mohamed,A., Hinton, G. E. and Penn, G. (2012)
Understanding how Deep Belief Networks perform acoustic modelling
ICASSP 2012, Kyoto.
[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]
