Daniel Tenbrinck

2024

• Bozorgnia F., Bungert L., Tenbrinck D.:
  The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme
  In: Journal of Scientific Computing 98 (2024), Article No.: 40
  ISSN: 0885-7474
  DOI: 10.1007/s10915-023-02425-w
  URL: https://link.springer.com/article/10.1007/s10915-023-02425-w
• Fazeny A., Tenbrinck D., Lukin K., Burger M.:
  Hypergraph p-Laplacians and Scale Spaces
  In: Journal of Mathematical Imaging and Vision (2024)
  ISSN: 0924-9907
  DOI: 10.1007/s10851-024-01183-0
  URL: https://link.springer.com/article/10.1007/s10851-024-01183-0

2023

• Fazeny A., Tenbrinck D., Burger M.:
  Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks
  International Conference on Scale Space and Variational Methods in Computer Vision (Santa Margherita di Pula, 21. May 2023 - 25. May 2023)
  In: SSVM 2023: Scale Space and Variational Methods in Computer Vision 2023
  DOI: 10.1007/978-3-031-31975-4_52
• Kabri S., Roith T., Tenbrinck D., Burger M.:
  Resolution-Invariant Image Classification Based on Fourier Neural Operators
  International Conference on Scale Space and Variational Methods in Computer Vision (Santa Margherita di Pula, 23. May 2023 - 25. May 2023)
  In: Scale Space and Variational Methods in Computer Vision 2023
  DOI: 10.1007/978-3-031-31975-4_18

2022

• Bungert L., Roith T., Tenbrinck D., Burger M.:
  A Bregman Learning Framework for Sparse Neural Networks
  In: Journal of Machine Learning Research (2022)
  ISSN: 1532-4435
  Open Access: https://www.jmlr.org/papers/v23/21-0545.html

2021

• Bergmann R., Herzog R., Silva Louzeiro M., Tenbrinck D., Vidal-Núñez J.:
  Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds
  In: Foundations of Computational Mathematics (2021)
  ISSN: 1615-3375
  DOI: 10.1007/s10208-020-09486-5
• Bungert L., Raab R., Roith T., Schwinn L., Tenbrinck D.:
  CLIP: Cheap Lipschitz Training of Neural Networks
  International Conference on Scale Space and Variational Methods in Computer Vision
  In: Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon (ed.): SSVM 2021: Scale Space and Variational Methods in Computer Vision, Cham: 2021
  DOI: 10.1007/978-3-030-75549-2_25
  URL: https://arxiv.org/abs/2103.12531
• Schwinn L., Nguyen A., Raab R., Bungert L., Tenbrinck D., Zanca D., Burger M., Eskofier B.:
  Identifying untrustworthy predictions in neural networks by geometric gradient analysis
  Conference on Uncertainty in Artificial Intelligence (UAI) (Online, 27. July 2021 - 30. July 2021)
  URL: https://arxiv.org/abs/2102.12196
• Schwinn L., Nguyen A., Raab R., Zanca D., Eskofier B., Tenbrinck D., Burger M.:
  Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
  International Joint Conference on Neural Networks (IJCNN) (Online, 18. July 2021 - 22. July 2021)
  DOI: 10.1109/ijcnn52387.2021.9534190

2020

• Gross-Thebing S., Truszkowski L., Tenbrinck D., Sanchez-Iranzo H., Camelo C., Westerich KJ., Singh A., Maier P., Prengel J., Lange P., Huewel J., Gaede F., Sasse R., Vos BE., Betz T., Matis M., Prevedel R., Luschnig S., Diz-Munoz A., Burger M., Raz E.:
  Using migrating cells as probes to illuminate features in live embryonic tissues
  In: Science Advances 6 (2020)
  ISSN: 2375-2548
  DOI: 10.1126/sciadv.abc5546

2019

• Bungert L., Burger M., Tenbrinck D.:
  Computing Nonlinear Eigenfunctions via Gradient Flow Extinction
  SSVM 2019 (Hofgeismar, 30. June 2019 - 4. July 2019)
  DOI: 10.1007/978-3-030-22368-7_23
  URL: https://arxiv.org/abs/1902.10414

2018

• Bergmann R., Tenbrinck D.:
  A Graph Framework for Manifold-valued Data
  In: Siam Journal on Imaging Sciences 11 (2018)
  ISSN: 1936-4954
  DOI: 10.1137/17M1118567

2017

• Bergmann R., Tenbrinck D.:
  Nonlocal Inpainting of Manifold-Valued Data on Finite Weighted Graphs
  International Conference on Geometric Science of Information (Mines ParisTech, Paris, 7. November 2017 - 9. November 2017)
  URL: https://arxiv.org/abs/1704.06424