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Dr. Christian Etmann

Ehemaliger Mitarbeiter der WG Industrial Mathematics, Research Training Group π3


Research Areas

Projects

  1. Neural networks in MALDI imaging (since 01.10.2016)
  2. BMBF-MaDiPath: Mass spectrometric profiling and grading for oncologic routine applications in pathology (01.10.2015 - 30.09.2018)

Courses (Selection)complete list

  1. Machine Learning (Sommersemester 2018)
  2. Mathematical Foundations of Machine Learning (Sommersemester 2019)

Theses (Selection)complete list

  1. A Representer Theorem for the Activation Functions of Neural Networks (Daniel Klosa)
  2. Deep-Learning-Konzepte zur Optimierung von ISTA-Verfahren (Alexander Denker)

Publications (Selection)complete list

  1. C. Etmann.
    Double Backpropagation with Applications to Robustness and Saliency Map Interpretability.
    Dissertationsschrift, Universität Bremen, 2020.
  2. C. Etmann.
    A Closer Look at Double Backpropagation.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/1906.06637

  3. C. Etmann, S. Lunz, P. Maaß, C. Schönlieb.
    On the Connection Between Adversarial Robustness and Saliency Map Interpretability.
    36th International Conference on Machine Learning, 09.06.-15.06.2019, Los Angeles, USA.
    PMLR 97, 97:1823-1832, 2019.

    online at: http://proceedings.mlr.press/v97/etmann19a.html

  4. C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maaß.
    Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/1912.05459

  5. J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte, J. Kriegsmann, P. Maaß.
    Deep Learning for Tumor Classification in Imaging Mass Spectrometry.
    Bioinformatics, 34(7):1215-1223, Oxford University Press, 2018.

    DOI: 10.1093/bioinformatics/btx724