Belief propagation on factor graph neural networks
Yin, Jialong
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https://hdl.handle.net/2142/109620
Description
Title
Belief propagation on factor graph neural networks
Author(s)
Yin, Jialong
Issue Date
2020-12-18
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Sanmi
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Belief Propagation
Graph Neural Networks
Abstract
Probabilistic graphical models are a statistical framework for conditionally dependent random variables with dependencies represented by graphs. A traditional method to perform inference over these random variables is Belief Propagation. Belief Propagation can be used to compute an exact solution for non-loopy factor graphs. However, when applied to loopy factor graphs, it only estimates marginal probabilities approximately. In this thesis, we propose a Graph Neural Networks (GNN) approach for belief propagation based on message passing mechanisms. In the proposed approach, representations and other functions are learned by the GNN. We apply this approach to the inference of loopy factor graphs. Furthermore, we show that learned representations and functions can also be generalized to factor graphs with different sizes and structures. The results show that our proposal has promising performance compared to the state of the art.
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