Bayesian Deep Learning for Graphs. (arXiv:2202.12348v1 [cs.LG])

The adaptive processing of structured data is a long-standing research topic
in machine learning that investigates how to automatically learn a mapping from
a structured input to outputs of various nature. Recently, there has been an
increasing interest in the adaptive processing of graphs, which led to the
development of different neural network-based methodologies. In this thesis, we
take a different route and develop a Bayesian Deep Learning framework for graph
learning. The dissertation begins with a review of the …

arxiv bayesian bayesian deep learning deep learning graphs learning