Prof. Dr. Daniel Tenbrinck

Professur im Themenfeld Data Science Department of Data Science Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Identifying untrustworthy predictions in neural networks by geometric gradient analysis (2021) Schwinn L, Nguyen A, Raab R, Bungert L, Tenbrinck D, Zanca D, Burger M, Eskofier B Conference contribution, Conference Contribution Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds (2021) Bergmann R, Herzog R, Silva Louzeiro M, Tenbrinck D, Vidal-Núñez J Journal article Using migrating cells as probes to illuminate features in live embryonic tissues (2020) Gross-Thebing S, Truszkowski L, Tenbrinck D, Sanchez-Iranzo H, Camelo C, Westerich KJ, Singh A, et al. Journal article Computing Nonlinear Eigenfunctions via Gradient Flow Extinction (2019) Bungert L, Burger M, Tenbrinck D Book chapter / Article in edited volumes A Graph Framework for Manifold-valued Data (2018) Bergmann R, Tenbrinck D Journal article Nonlocal Inpainting of Manifold-Valued Data on Finite Weighted Graphs (2017) Bergmann R, Tenbrinck D Conference contribution, Conference Contribution Image segmentation with physical noise models (2016) Tenbrinck D, Jiang X Book chapter / Article in edited volumes Image Segmentation with Arbitrary Noise Models by Solving Minimal Surface Problems (2015) Tenbrinck D, Jiang X Journal article, Original article Solving Minimal Surface Problems on Surfaces and Point Clouds (2015) Tenbrinck D, Lozes F, Elmoataz A Conference contribution, Conference Contribution On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing (2015) Elmoataz A, Toutain M, Tenbrinck D Journal article