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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/19226

Title: Learning Deep Generative Models
Authors: Salakhutdinov, Ruslan
Advisor: Hinton, Geoffrey
Department: Computer Science
Keywords: computer science
probabilistic graphical models
Issue Date: 2-Mar-2010
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.
URI: http://hdl.handle.net/1807/19226
Appears in Collections:Doctoral
Department of Computer Science - Doctoral theses

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