Composable, Distributed-state Models for High-dimensional Time Series

Composable, Distributed-state Models for High-dimensional Time Series

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In this thesis we develop a class of nonlinear generative models for high-dimensional time series. The first key property of these models is their distributed, or "componential" latent state, which is characterized by binary stochastic variables which interact to explain the data. The seco...

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Bibliographic Details
Main Author: Taylor, Graham William
Other Authors: Hinton, Geoffrey
Language:en_ca
Published: 2009
Subjects:
machine learning
time series
neural networks
unsupervised learning
restricted Boltzmann machines
hidden Markov models
graphical models
motion capture
dynamical models
generative models
computer vision
tracking
0984
Online Access:http://hdl.handle.net/1807/19238

Internet

http://hdl.handle.net/1807/19238

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