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Publications of Dr. Jens Behrmann

Book Chapters (1)

  1. J. Behrmann, M. Schmidt, J. Wildner, P. Maaß, S. Schmale.
    Purity Assessment of Pellets Using Deep Learning.
    German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Eds.), Mathematics in Industry, pp. 29-34, Springer Verlag, 2022.

    DOI: 10.1007/978-3-030-81455-7_6

Articles (2)

  1. C. Janßen, T. Boskamp, L. Hauberg-Lotte, J. Behrmann, S. Deininger, M. Kriegsmann, K. Kriegsmann, G. Steinbuß, H. Winter, T. Muley, R. Casadonte, J. Kriegsmann, P. Maaß.
    Robust subtyping of non-small cell lung cancer whole sections through MALDI mass spectrometry imaging.
    Proteomics - Clinical Applications, PRCA2208 , 2022.

    DOI: 10.1002/prca.202100068

  2. 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

Preprints (3)

  1. J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
    Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2006.09347

  2. 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

  3. J. Behrmann, S. Dittmer, P. Fernsel, P. Maaß.
    Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/1806.09730

PhD/Habilitation Thesis (1)

  1. J. Behrmann.
    Principles of Neural Network Architecture Design: Invertibility and Domain Knowledge.
    Dissertationsschrift, Universität Bremen, 2019.

    online at: https://elib.suub.uni-bremen.de/peid/D00108536.html

Proceedings (8)

  1. 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, Vienna, Austria.

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

  2. F. Tramer, J. Behrmann, N. Carlini, N. Papernot, J. Jacobsen.
    Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.
    International Conference on Machine Learning (ICML), 12.07 - 18.07.2020, Vienna, Austria.

    online at: https://arxiv.org/abs/2002.04599

  3. J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
    On the Invertibility of Invertible Neural Networks.
    NeurIPS workshop on Machine Learning with Guarantees, 2019.

    online at: https://sites.google.com/view/mlwithguarantees/accepted-papers

  4. J. Jacobsen, J. Behrmann, R. Zemel, M. Bethge.
    Excessive Invariance Causes Adversarial Vulnerability.
    International Conference on Learning Representations (ICLR), 2019.

    online at: https://openreview.net/forum?id=BkfbpsAcF7

  5. J. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot.
    Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.
    SafeML Workshop, ICLR, 2019.

    online at: https://arxiv.org/abs/1903.10484

  6. J. Behrmann, W. Grathwohl, R. T. Chen, D. Duvenaud, J. Jacobsen.
    Invertible Residual Networks.
    International Conference on Machine Learning (ICML).
    Proceedings of Machine Learning Research, 97:573-582, 2019.

    Long Oral

    online at: http://proceedings.mlr.press/v97/behrmann19a.html

  7. R. T. Chen, J. Behrmann, D. Duvenaud, J. Jacobsen.
    Residual Flows for Invertible Generative Modeling.
    Advances in Neural Information Processing Systems (NeurIPS).
    32, pp. 9916--9926, 2019.


    online at: https://papers.nips.cc/paper/9183-residual-flows-for-invertible-generative-modeling

  8. T. Boskamp, D. Lachmund, J. Oetjen, Y. Hernandez-Cordero, J. Behrmann, J. H. Kobarg, R. Casadonte, J. Kriegsmann, P. Maaß.
    Visualizing MALDI TOF datasets of FFPE tissue samples for the purpose of quality assessment and comparison.
    OurCon IV - 2016, 17.10.-21.10.2016, Ustron, Poland.

    online at: http://www.bioradint.eu/ourcon_public/papersview.php?showdetail=&paper_id=45