

Subgraph-based networks for expressive, efficient, and domain-independent graph learning
De Haggai Maron


Optimal transport for graphs: definitions, applications to graph-signal processing
De Nicolas Courty
Apparaît dans la collection : Learning and Optimization in Luminy - LOL2022 / Apprentissage et Optimisation à Luminy - LOL2022
The statistical problem of estimating a Sobolev function using a deep network is studied using the Neuberger theorem and recent approximation results by Gurhing Kutyniok and Petersen. The problem is addressed by decoupling the statistical and the approximation problems and is shown to boil down to the computation of the Sobolev norm of bump functions.