Dr. Christian Etmann

Research Areas

• Deep Learning
• Imaging mass spectrometry
• Regularization in Machine Learning
• Nonsmooth Analysis

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, 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

3. 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

4. C. Etmann.
   A Closer Look at Double Backpropagation.
   Zur Veröffentlichung eingereicht.

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

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