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Summary: Learning the Structure of Deep Sparse Graphical Models
Ryan Prescott Adams Hanna M. Wallach Zoubin Ghahramani
University of Toronto University of Massachusetts Amherst University of Cambridge
Abstract
Deep belief networks are a powerful way
to model complex probability distributions.
However, it is difficult to learn the structure
of a belief network, particularly one with hid-
den units. The Indian buffet process has been
used as a nonparametric Bayesian prior on
the structure of a directed belief network with
a single infinitely wide hidden layer. Here, we
introduce the cascading Indian buffet process
(CIBP), which provides a prior on the struc-
ture of a layered, directed belief network that
is unbounded in both depth and width, yet
allows tractable inference. We use the CIBP
prior with the nonlinear Gaussian belief net-
work framework to allow each unit to vary
its behavior between discrete and continuous
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