Generating Rectifiable Measures through Neural Networks

Erwin Riegler, Alex Bühler, Yang Pan +1

We derive universal approximation results for the class of (countably) $m$-rectifiable measures. Specifically, we prove that $m$-rectifiable measures can be approximated as push-fo…