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Dr. Alexander Denker

Ehemaliger Mitarbeiter der WG Industrial Mathematics, Research Training Group π3

Email: adenker

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, A. Denker, Z. Kereta.
    Convergence Properties of Score-Based Models using Graduated Optimisation for Linear Inverse Problems.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2404.18699

  2. F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
    PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
    Inverse Problems, 39(6), 2023.

    online at: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

  3. 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

  4. 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

  5. A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
    Score-Based Generative Models for PET Image Reconstruction.
    Erscheint in Machine Learning for Biomedical Imaging

    online at: https://arxiv.org/abs/2308.14190