Item – Theses Canada

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OCLC number
1033017332
Link(s) to full text
LAC copy
Author
Salakhutdinov, Ruslan,1979-
Title
Learning Deep Generative Models.
Degree
University of Toronto, NO_R
Publisher
Toronto : University of Toronto, NO_R.
Description
1 online resource
Notes
Includes bibliographical references.
Abstract
Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that the learned high-level feature representations can be successfully applied in a wide spectrum of application domains, including visual object recognition, information retrieval, and classification and regression tasks. In addition, similar methods can be used for nonlinear dimensionality reduction.
Other link(s)
hdl.handle.net
tspace.library.utoronto.ca
Subject
computer science.
probabilistic graphical models.