BIOGRAPHICAL INFORMATION
......Curriculum Vitae .pdf
......Biographical sketch
......Brief Bio
......Photograph1 (.jpg)
......Photograph2 (.jpg)
 
PUBLICATIONS
......Publications by year
......Slides of public talks
 
2012 COURSERA COURSE LECTURES:
Neural Networks for Machine Learning
....Lectures(.mp4)
....Lecture Slides(.pptx or .pdf))
 
OLD UNIVERSITY OF TORONTO COURSES
....csc321 Spring 2013(undergrad)
....csc2535 Spring 2013(graduate)
 
VIDEO TALKS & TUTORIALS
....YouTube (2012) Brains, Sex and Machine Learning (1hr)
....YouTube (2007) The Next Generation of Neural Networks (1hr)
....YouTube (2010) Recent Developments in Deep Learning (1hr)
.... Interview on CBC radio "Quirks and Quarks" Feb 11 2011
.... Interview on CBC radio "The Current", May 5 2015
....Tutorial (2009) Deep Belief Nets (3hrs) ppt pdf readings
....Workshop Talk (2007) How to do backpropagation in a brain (20mins) ppt2007 pdf2007 ppt2014 pdf2014
 
OLD TUTORIAL SLIDES
....2011 NIPS workshop talk pdf ppt
........paper on Transforming Autoencoders
....2007 NIPS tutorial html ppt ps pdf
........Readings: 2007 NIPS tutorial
....CIFAR Summer School 2007
....CIAR Summer School 2006
....CIFAR Summer School 2005
....List of Past Tutorials
 
MOVIES
....generating digits
....speaking with a glove (Sidney Fels)
 
MATLAB CODE
.... Matlab for Science paper
....t-SNE software
....trajectory from motor program
....ink from trajectory
....introduction to python
 
SUPERVISION
....Current PhD and Master's Students
....Former PhD Students
....Former Master's Students
....PostDocs
 
MACHINE LEARNING AT TORONTO
....learning.cs.toronto.edu
 
HOME PAGE (top level)
....www.cs.toronto.edu/~hinton
 

Geoffrey E. Hinton

Department of Computer Science
  email: geoffrey [dot] hinton [at] gmail [dot] com
University of Toronto   voice: send email
6 King's College Rd.   fax: scan and send email
Toronto, Ontario  
 

Information for prospective students, postdocs and visitors:
I will not be taking any more students, postdocs or visitors.

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

Using big data to make people vote against their own interests

Basic papers on deep learning

LeCun, Y., Bengio, Y. and Hinton, G. E. (2015)
Deep Learning
Nature, Vol. 521, pp 436-444. [pdf]

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]

Hinton, G. E. (2014)
Where do features come from?.
Cognitive Science, Vol. 38(6), pp 1078-1101. [pdf]

Recent Papers

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]

Kosiorek, A. R., Sabour, S., Teh, Y. W. and Hinton, G. E.
Stacked Capsule Autoencoders
Advances in Neural Information Processing Systems 32 [pdf]

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]

Muller, R., Kornblith, S. and Hinton G. (2019)
When Does Label Smoothing Help?
Advances in Neural Information Processing Systems 32 [pdf]

Deng, B., Kornblith, S. and Hinton, G. (2019)
Cerberus: A multi-headed derenderer.
3D Scene Understanding Workshop, CVPR 2019 [pdf]

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]

Kornblith, S., Norouzi, M., Lee, H. and Hinton, G. (2019)
Similarity of neural network representations revisited
ICML-2019 [pdf]

Hinton, G. E., Sabour, S. and Frosst, N.
Matrix Capsules with EM Routing
ICLR-2018 [pdf]

Kiros, J. R., Chan, W. and Hinton, G. E.
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search
ACL-2018 [pdf]

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]

Guan, M. Y., Gulshan, V., Dai, A. M. and Hinton, G. E.
Who Said What: Modeling Individual Labelers Improves Classification
AAAI-2018 [pdf]

Sabour, S., Frosst, N. and Hinton, G. E.
Dynamic Routing between Capsules
NIPS-2017, [pdf]

Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017)
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
arXiv preprint arXiv:1701.06538 [pdf]

Ba, J. L., Hinton, G. E., Mnih, V., Leibo, J. Z. and Ionescu, C. (2016)
Using Fast Weights to Attend to the Recent Past
NIPS-2016, arXiv preprint arXiv:1610.06258v2 [pdf]

Ba, J. L., Kiros, J. R. and Hinton, G. E. (2016)
Layer normalization
Deep Learning Symposium, NIPS-2016, arXiv preprint arXiv:1607.06450 [pdf]

2021 commencement address at IIT Mumbai    

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

Doing analogies by using vector algebra on word embeddings    

My favorite Gary Marcus quote    

A new concept of healing from the people who design unimprovised explosive devices