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Title:Belief propagation on factor graph neural networks
Author(s):Yin, Jialong
Advisor(s):Koyejo, Sanmi
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(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.
Issue Date:2020-12-18
Rights Information:Copyright 2020 Jialong Yin
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12

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