activity
20172026
most citedCLIP: Cheap Lipschitz Training of Neural Networks

21 citations · 75 across the 16 of their papers we have counts for

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7 papers · 1 filter

math.NA2025

On MAP estimates and source conditions for drift identification in SDEs

Daniel Tenbrinck, Nikolas Uesseler, Philipp Wacker +1

We consider the inverse problem of identifying the drift in an SDE from observations of its solution at distinct time points. We derive a corresponding MAP estimate, we p…

math.NA2020★ 5 cited

The Infinity Laplacian eigenvalue problem: reformulation and a numerical scheme

Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck

In this work, we present an alternative formulation of the higher eigenvalue problem associated to the infinity Laplacian, which opens the door for numerical approximation of eigen…

math.NA2019★ 3 cited

Fenchel Duality Theory and A Primal-Dual Algorithm on Riemannian Manifolds

Ronny Bergmann, Roland Herzog, Maurício Silva Louzeiro +2

This paper introduces a new notion of a Fenchel conjugate, which generalizes the classical Fenchel conjugation to functions defined on Riemannian manifolds. We investigate its prop…

math.NA2019★ 5 cited

Variational Graph Methods for Efficient Point Cloud Sparsification

Daniel Tenbrinck, Fjedor Gaede, Martin Burger

In recent years new application areas have emerged in which one aims to capture the geometry of objects by means of three-dimensional point clouds. Often the obtained data consist…

math.NA2019

Computing Nonlinear Eigenfunctions via Gradient Flow Extinction

Leon Bungert, Martin Burger, Daniel Tenbrinck

In this work we investigate the computation of nonlinear eigenfunctions via the extinction profiles of gradient flows. We analyze a scheme that recursively subtracts such eigenfunc…

math.NA2017★ 4 cited

Nonlocal Inpainting of Manifold-valued Data on Finite Weighted Graphs

Ronny Bergmann, Daniel Tenbrinck

Recently, there has been a strong ambition to translate models and algorithms from traditional image processing to non-Euclidean domains, e.g., to manifold-valued data. While the t…

math.NA2017★ 16 cited

A Graph Framework for Manifold-valued Data

Ronny Bergmann, Daniel Tenbrinck

Graph-based methods have been proposed as a unified framework for discrete calculus of local and nonlocal image processing methods in the recent years. In order to translate variat…