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Understanding the dynamics of message passing algorithms: a free probability heuristics
Published 3 Feb 2020 in cond-mat.stat-mech, cond-mat.dis-nn, cs.LG, and stat.ML | (2002.02533v1)
Abstract: We use freeness assumptions of random matrix theory to analyze the dynamical behavior of inference algorithms for probabilistic models with dense coupling matrices in the limit of large systems. For a toy Ising model, we are able to recover previous results such as the property of vanishing effective memories and the analytical convergence rate of the algorithm.
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