Prof. Dr. Daniel Tenbrinck

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

Hypergraph p-Laplacians and Scale Spaces (2024) Fazeny A, Tenbrinck D, Lukin K, Burger M Journal article, Original article The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme (2024) Bozorgnia F, Bungert L, Tenbrinck D Journal article, Original article Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks (2023) Fazeny A, Tenbrinck D, Burger M Conference contribution Resolution-Invariant Image Classification Based on Fourier Neural Operators (2023) Kabri S, Roith T, Tenbrinck D, Burger M Conference contribution A Bregman Learning Framework for Sparse Neural Networks (2022) Bungert L, Roith T, Tenbrinck D, Burger M Journal article, Original article CLIP: Cheap Lipschitz Training of Neural Networks (2021) Bungert L, Raab R, Roith T, Schwinn L, Tenbrinck D Conference contribution, Conference Contribution Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks (2021) Schwinn L, Nguyen A, Raab R, Zanca D, Eskofier B, Tenbrinck D, Burger M Conference contribution, Conference Contribution 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