The Taub Faculty of Computer Science Events and Talks
Maxim Fishman (EE, Technion)
Wednesday, 29.06.2022, 11:30
Despite their growing popularity, graph neural networks (GNNs) still suffer from multiple unsolved problems, including lack of embedding expressiveness, propagation of information to distant nodes, and training on large-scale graphs. Understanding the roots of and providing solutions for such problems require developing analytic tools and techniques. In this talk we provide a measure theoretic point of view for the above-mentioned problems, and derive a notion of “recoverability” which will serve us as a tool for GNN embedding analysis, unsupervised graph representation learning and regularization. At the end of the talk, we will show a tight relationship between recoverability loss minimization and mutual information maximization.
M.Sc. student under the supervision of Prof. Avi Mendelson and Dr. Chaim Baskin.