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Activity Number: 74
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319830
Title: An Exposition on the Propriety of Restricted Boltzmann Machines
Author(s): Andrea Kaplan* and Daniel Nordman and Stephen Vardeman
Companies: Iowa State University and Iowa State University and Iowa State University
Keywords: undirected graphical models ; model degeneracy ; deep learning ; Boltzmann machines ; fitting highly flexible models

A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete random variables, specified as having two layers, a hidden and a visible layer with no conditional dependency within a layer. In recent years, RBMs have risen to prominence due to their connection to deep learning - by treating a hidden layer of one RBM as the visible layer in a second RBM a deep architecture can be created. The method is claimed to have the ability to learn very complex and rich structures in data, making these models attractive for supervised learning. However, the generative behavior of RBMs has largely been unexplored. In this presentation, we discuss the relationship between parameter specification and the prevalence of degenerate models as well as an exposition of the difficulties in fitting such highly flexible models.

Authors who are presenting talks have a * after their name.

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