Graph signal recovery using restricted Boltzmann machines

Given: (a) clean training dataset, (b) trained graph machine learning pipeline, and (c) noisy test dataset; how do we make the pipeline robust to such noise? In an attempt to answer this question, we propose a modification to the pipeline that exploits both the content addressability of a restricted Boltzmann machine and the message passing capabilities of a graph neural network. We investigate which of the hidden layers of the pipeline is best...