Geoffrey E. Hinton

I now work part-time for Google as a Distinguished Researcher and part-time for the University of Toronto as a Distinguished Professor. For much of the year, I work at the University from 9.30am to 1.30pm and at the Google Toronto office at 111 Richmond Street from 2.00pm to 6.00pm. I also spend several months per year working full-time for Google in Mountain View, California.

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

Srivastava, N., Salakhutdinov, R. R. and Hinton, G. E. Modeling Documents with a Deep Boltzmann Machine
In Uncertainty in Artificial Intelligence (UAI 2013) [pdf]

Graves, A., Mohamed, A. and Hinton, G. E. (2013)
Speech Recognition with Deep Recurrent Neural Networks
In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf]

Dahl, G. E., Sainath, T. N. and Hinton, G. E. (2013)
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]

M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G. Hinton (2013)
On Rectified Linear Units for Speech Processing
In IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013. [pdf]

Deng, L., Hinton, G. E. and Kingsbury, B. (2013)
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]

Ranzato, M., Mnih, V., Susskind, J. and Hinton, G. E. (2013)
Modeling Natural Images Using Gated MRFs
IEEE Trans. Pattern Analysis and Machine Intelligence, to appear [pdf]

Sutskever, I., Martens, J., Dahl, G. and Hinton, G. E. (2013)
On the importance of momentum and initialization in deep learning
In 30th International Conference on Machine Learning, Atlanta, USA [pdf]

Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2013)
Tensor Analyzers
In 30th International Conference on Machine Learning, Atlanta, USA [pdf]

Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012)
ImageNet Classification with Deep Convolutional Neural Networks
Advances in Neural Information Processing 25, MIT Press, Cambridge, MA [pdf]

Salakhutdinov, R. R. and Hinton, G. E. (2012)
A Better Way to Pretrain Deep Boltzmann Machines
Advances in Neural Information Processing 25, MIT Press, Cambridge, MA [pdf]

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]

Joseph Turian's map of 2500 English words produced by using t-SNE on the word feature vectors learned by Collobert & Weston, ICML 2008    

Is James Murdoch honest?