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20182025
most citedAsymptotic Profiles of Nonlinear Homogeneous Evolution Equations of Gradient Flow Type

22 citations · 123 across the 18 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

math.AP2021★ 2 cited

The inhomogeneous -Laplacian equation with Neumann boundary conditions in the limit

Leon Bungert

We investigate the limiting behavior of solutions to the inhomogeneous -Laplacian equation subject to Neumann boundary conditions. For right hand sides which are…

math.AP2021★ 10 cited

Eigenvalue Problems in : Optimality Conditions, Duality, and Relations with Optimal Transport

Leon Bungert, Yury Korolev

In this article we characterize the eigenvalue problem associated to the Rayleigh quotient $\left.{\|\nabla u\|_{\mathrm{L}^\infty}}\middle/{\|u\|_\infty}\right…

cs.LG2021

Neural Architecture Search via Bregman Iterations

Leon Bungert, Tim Roith, Daniel Tenbrinck +1

We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations. Starting from a sparse neural network our gradient-based one-shot algorithm gradually…

math.NA2021★ 6 cited

Gradient Flows and Nonlinear Power Methods for the Computation of Nonlinear Eigenfunctions

Leon Bungert, Martin Burger

This chapter describes how gradient flows and nonlinear power methods in Banach spaces can be used to solve nonlinear eigenvector-dependent eigenvalue problems, and how convergence…

cs.LG2021★ 2 cited

A Bregman Learning Framework for Sparse Neural Networks

Leon Bungert, Tim Roith, Daniel Tenbrinck +1

We propose a learning framework based on stochastic Bregman iterations, also known as mirror descent, to train sparse neural networks with an inverse scale space approach. We deriv…

cs.LG2021★ 21 cited

CLIP: Cheap Lipschitz Training of Neural Networks

Leon Bungert, René Raab, Tim Roith +2

Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability. For instance, DNNs are vulne…

cs.LG2021★ 2 cited

Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

Leo Schwinn, An Nguyen, René Raab +5

The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in sa…