Geoffrey Hinton's former PhD students
(with year of graduation and thesis title)

Peter Brown (1987)
The Acoustic-Modeling Problem in Automatic Speech Recognition.

David Ackley (1987)
Stochastic Iterated Genetic Hillclimbing.

Mark Derthick (1988)
Mundane Reasoning by Parallel Constraint Satisfaction.

Richard Szeliski (1988)
Bayesian Modeling of Uncertainty in Low-Level Vision. (co-advisor)

Kevin Lang (1989)
Phoneme Recognition Using Time-Delay Neural Nets.

Steven Nowlan (1991)
Soft Competitive Adaptation

David Plaut (1991)
Connectionist Neuropsychology.

Conrad Galland (1992)
Learning in Deterministic Boltzmann Machine Networks.

Susanna Becker (1992)
An Information Theoretic Unsupervised Learning Algorithm for Neural Networks.

Richard Zemel (1994)
A Minimum Description Length Framework for Unsupervised Learning.

Tony Plate (1994)
Distributed Representations and Nested Compositional Structure.

Sidney Fels (1994)
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks.

Christopher Williams (1994)
Combining Deformable Models and Neural Networks for Handprinted Digit Recognition.

Radford Neal (1994)
Bayesian Learning in Neural Networks

Carl Rasmussen (1996)
Evaluation of Gaussian Processes and Other Methods for Non-linear Regression.

Brendan Frey (1997)
Graphical Models for Machine Learning and Digital Communication.

Evan Steeg (1997)
Automated Motif Discovery in Protein Structure Prediction.

Radek Grzeszczuk (1998)
NeuroAnimator: Fast neural network emulation and control of physics-based models. (co-adviser)

Brian Sallans (2002)
Reinforcement Learning for Factored Markov Decision Processes.

Sageev Oore (2002)
Digital Marionette: Augmenting Kinematics with Physics for Multi-Track Desktop Performance Animation

Andrew Brown (2002)
Product Models for Sequences

Alberto Paccanaro (2002)
Learning Distributed Representations of Relational Data using Linear Relational Embedding

Yee Whye Teh (2003)
Bethe Free Energy and Contrastive Divergence Approximations for Undirected Graphical Models

Simon Osindero (2004)
Contrastive Topographic Models: Energy-based density models applied to the understanding of sensory coding and cortical topography. (co-advisor)

Roland Memisevic (2007)
Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data

Ruslan Salakhutdinov (2009)
Learning deep generative models.

Graham Taylor (2009)
Composable, distributed-state models for high-dimensional time-series.

Andriy Mnih (2009)
Learning distributed representations for language modeling and collaborative filtering

Vinod Nair (2010)
Visual object recognition using generative models of images.

Josh Susskind (2011)
Interpreting faces with neurally inspired generative models.

Ilya Sutskever (2012)
Training recurrent neural networks.

Vlad Mnih (2013)
Machine learning for aerial image labeling.

Navdeep Jaitly (2014)
Exploring Deep Learning Methods for discovering features in speech signals.

Tijmen Tieleman (2014)
Optimizing neural networks that generate images.

George Dahl (2015)
Deep Learning Approaches to Problems in Speech Recognition, Computational Chemistry and Natural Language Processing.