Schedule-free variational message passing for Bayesian filtering

Abstract

Tools for message passing on factor graphs typically employ a scheduling procedure, in which a separate algorithm or compiler takes the model description and returns which nodes should pass messages where at what time. This can be sufficiently expensive to form a bottleneck. Moreover, it’s not a biologically plausible mechanism for governing message passing. I explore the possibility of passing messages without a scheduler. A designated terminal node should pass an initial message, which will arrive at an initial variable. The corresponding belief is updated, a local Free Energy is computed and the belief is emitted to neighbouring factor nodes. From there on out, whenever an updated belief arrives at a factor node, the node fires messages to all other variables if the local Free Energy surpasses a threshold. If not, the node becomes silent.

Publication
Neuromatch Conference 2020
Date
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