Dr. Alexander Denker

Ehemaliger Mitarbeiter der WG Industrial Mathematics, Research Training Group π³


Information: Email ends with @uni-bremen.de

Theses (Selection)complete list

1. Active learning for semantic segmentation in digital pathology (Jannik Wildner)
2. Adversarial Examples in Deep-Learning-Rekonstruktionen am Beispiel von Computer-Tomographie (Fabian Schönfeld)
3. Fehlererkennung und –segmentierung von Stahlcoils unter Verwendung des Contrastive Learnings (Dennis Hottendorff)

Publications (Selection)complete list

1. P. Fernsel, Z. Kereta, A. Denker.
   Convergence Properties of Score-Based Models using Graduated Optimisation for Linear Inverse Problems.
   2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 22.09.-25.09.2024, London, UK.
   IEEE, pp. 1-6, 2024.

   DOI: 10.1109/MLSP58920.2024.10734770

2. A. Denker.
   Invertible Neural Networks and Normalizing Flows for Image Reconstruction.
   Dissertationsschrift, Universität Bremen, 2024.

   DOI: 10.26092/elib/2921

3. A. Denker, Z. Kereta, I. Singh, T. Freudenberg, T. Kluth, B. Maass, S. Arridge.
   Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data.
   Applied Mathematics for Modern Challenges, 2(2):119-139, 2024.

   DOI: 10.3934/ammc.2024005

4. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
   Invertible residual networks in the context of regularization theory for linear inverse problems.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online at: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

5. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
   Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
   Applied Mathematics for Modern Challenges, 1(2), 2023.

   DOI: 10.3934/ammc.2023007

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