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Alexander Denker

Wissenschaftlicher Mitarbeiter der AG Technomathematik, Research Training Group π3

Raum: MZH 2285
E-Mail: adenker@uni-bremen.de
Telefon: (0421) 218-63897

Publikationen (Auswahl)vollständige Liste

  1. A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
    Conditional Invertible Neural Networks for Medical Imaging .
    MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021.

    DOI: 10.3390/jimaging7110243

  2. M. Schmidt, A. Denker, J. Leuschner.
    The Deep Capsule Prior - advantages through complexity.
    GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
    Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.

    DOI: 10.1002/pamm.202100166

  3. R. Barbano, J. Leuschner, M. Schmidt, A. Denker, P. Maaß, B. Jin.
    Is Deep Image Prior in Need of a Good Education?
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2111.11926

  4. J. Leuschner, M. Schmidt, P. Ganguly, V. Andriiashen, S. Coban, A. Denker, D. Bauer, A. Hadjifaradji, K. Batenburg, B. Maass, M. von Eijnatten.
    Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
    MDPI Journal of Imaging, 7(3), 44 S., 2021.

    DOI: 10.3390/jimaging7030044
    online unter: https://www.mdpi.com/2313-433X/7/3/44

  5. A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
    Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
    ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.

    online unter: https://invertibleworkshop.github.io/accepted_papers/index.html