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) (co-advised by Takeo Kanade)
Bayesian Modeling of Uncertainty in Low-Level Vision.
Phoneme Recognition Using Time-Delay Neural Nets.
Steven Nowlan (1991)
Soft Competitive Adaptation
David Plaut (1991)
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) (co-advised by Demitri Terzopoulos)
NeuroAnimator: Fast neural network emulation and control of
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
Product Models for Sequences
Alberto Paccanaro (2002)
Learning Distributed Representations of Relational Data using Linear
Bethe Free Energy and Contrastive Divergence Approximations for
Undirected Graphical Models
(2004) (co-advised by Peter Dayan)
Contrastive Topographic Models: Energy-based density models applied to
the understanding of sensory coding and cortical topography. (co-advisor)
Non-linear Latent Factor Models for Revealing Structure in
Ruslan Salakhutdinov (2009)
Learning deep generative models.
Graham Taylor (2009)
(co-advised by Sam Roweis)
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)
(co-advised by Adam Anderson)
Interpreting faces with neurally inspired generative models.
Ilya Sutskever (2012)
Training recurrent neural networks.
(2013) (co-advised by Gerald Penn)
Deep Neural Network Acoustic Models for Automatic Speech Recognition
Vlad Mnih (2013)
Machine learning for aerial image labeling.
Navdeep Jaitly (2014)
Exploring Deep Learning Methods for discovering features in speech
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) (co-advised by Russ Salakhutdinov)
Learning Generative Models using Structured Latent Variables.
(2016) (co-advised by Russ Salakhutdinov)
Deep Learning Models for Unsupervised and Transfer Learning.