Logo Uni Bremen

Zentrum für Industriemathematik

ZeTeM > Über das ZeTeM > Mitarbeiter*innen > Dr. Jens Behrmann > Publikationen

Kontakt Sitemap Impressum [ English | Deutsch ]

Publikationen von Dr. Jens Behrmann

Buchkapitel (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 (Hrsg.), Mathematics in Industry, S. 29-34, Springer Verlag, 2022.

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

Zeitschriftenartikel (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 unter: 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 unter: 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 unter: https://arxiv.org/abs/1806.09730

Qualifikationsarbeiten (1)

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

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

Tagungsbeiträge (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, Wien, Österreich.

    online unter: 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, Wien, Österreich.

    online unter: 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 unter: 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 unter: 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 unter: 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 unter: 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, S. 9916--9926, 2019.

    Spotlight

    online unter: 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, Polen.

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