3 citations · 5 across the 3 of their papers we have counts for
3 papers
math.NA2023
Measure transport via polynomial density surrogates
Josephine Westermann, Jakob Zech
We discuss an algorithm to compute transport maps that couple the uniform measure on with a specified target distribution on . The primary objectives are eit…
math.ST2023★ 3 cited
Distribution learning via neural differential equations: a nonparametric statistical perspective
Youssef Marzouk, Zhi Ren, Sven Wang +1
Ordinary differential equations (ODEs), via their induced flow maps, provide a powerful framework to parameterize invertible transformations for the purpose of representing complex…
math.NA2023★ 2 cited
Deep Operator Network Approximation Rates for Lipschitz Operators
Christoph Schwab, Andreas Stein, Jakob Zech
We establish universality and expression rate bounds for a class of neural Deep Operator Networks (DON) emulating Lipschitz (or Hölder) continuous maps $\mathcal G:\mathcal X\to\ma…