output
20002022
most citedApproximation spaces of deep neural networks

30 citations

researchers with a paper here
collaborating institutions

27 papers

math.AP2022★ 4 cited

Extending linear growth functionals to functions of bounded fractional variation

Hidde Schönberger

In this paper we consider the minimization of a novel class of fractional linear growth functionals involving the Riesz fractional gradient. These functionals lack the coercivity p…

math.PR2022★ 4 cited

Approximation of the number of descendants in branching processes

Anton A Kutsenko

We discuss approximations of the relative limit densities of descendants in Galton--Watson processes that follow from the Karlin--McGregor near-constancy phenomena. These approxima…

math.CA2022★ 3 cited

Banach gradient flows for various families of knot energies

Hannes Matt, Daniel Steenebrügge, Heiko von der Mosel

We establish long-time existence of Banach gradient flows for generalised integral Menger curvatures and tangent-point energies, and for O'Hara's self-repulsive potentials $E^{α,p}…

math.CA2021★ 9 cited

A note on exponential Riesz bases

Andrei Caragea, Dae Gwan Lee

We prove that if , , are disjoint intervals in with the property that the numbers are l…

cs.LG2021★ 13 cited

Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction

Dominik Stöger, Mahdi Soltanolkotabi

Recently there has been significant theoretical progress on understanding the convergence and generalization of gradient-based methods on nonconvex losses with overparameterized mo…

math.NA2021★ 7 cited

Structural Sparsity in Multiple Measurements

Florian Boßmann, Sara Krause-Solberg, Johannes Maly +1

We propose a novel sparsity model for distributed compressed sensing in the multiple measurement vectors (MMV) setting. Our model extends the concept of row-sparsity to allow more…