Scalable Learning and Inference in Hierarchical Models of... October 9, 2007

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Google TechTalks
January 17, 2006

Tom Dean

ABSTRACT
Borrowing insights from computational neuroscience, we present a class of generative models well suited to modeling perceptual processes and an algorithm for learning their parameters that promises to scale to learning very large models. The models are hierarchical, composed of multiple levels, and allow input only at the lowest level, the base of the hierarchy. Connections within a level are generally local and may or may not be directed. Connections between levels are directed and generally do not span multiple levels.

The learning algorithm falls within the general family of expectation maximization algorithms. Parameter estimation...