Geoffrey E. Hinton

Former PhD Students

• 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-advised by Takeo Kanade)
• 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-advised by Demitri Terzopoulos)
• 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-advised by Peter Dayan)
• 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. (co-advised by Sam Roweis)
• 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. (co-advised by Adam Anderson)
• Ilya Sutskever
  2012
  Training recurrent neural networks.
• Abdel-rahman Mohamed
  2013
  Deep Neural Network Acoustic Models for Automatic Speech Recognition (co-advised by Gerald Penn)
• 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.
• (Charlie) Yichuan Tang
  2015
  Learning Generative Models using Structured Latent Variables. (co-advised by Russ Salakhutdinov)
• Nitish Srivastava
  2016
  Deep Learning Models for Unsupervised and Transfer Learning. (co-advised by Russ Salakhutdinov)
• Jimmy Lei Ba
  2020
  Learning to Attend with Neural Networks.