Publications of WG Industrial Mathematics

Articles (6)

1. J. Clemens, T. Kluth, T. Reineking.
   β - SLAM: Simultaneous Localization an Grid Mapping with Beta Distributions.
   Information Fusion, 52:62-75, Elsevier, 2019.

   DOI: 10.1016/j.inffus.2018.11.005

2. T. Kluth, B. Jin.
   Enhanced Reconstruction in Magnetic Particle Imaging by Whitening and Randomized SVD Approximation.
   Physics in Medicine and Biology, Article ID 125026 64(12), 2019.

   DOI: 10.1088/1361-6560/ab1a4f

3. A. Ardestani, S. Li, K. Annamalai, B. Lupse, S. Geravandi, A. Dobrowolski, S. Yu, S. Zhu, T. D. Baguley, M. Surakattula, J. Oetjen, L. Hauberg-Lotte, R. Herranz, S. Awal, D. Altenhofen, V. Nguyen-Tran, S. Joseph, P. G. Schultz, A. K. Chatterjee, N. Rogers, M. S. Tremblay, W. Shen, K. Maedler.
   Neratinib protects pancreatic beta cells in diabetes.
   Nature Communications, 10(5015), 2019.

   DOI: 10.1038/s41467-019-12880-5.
   online at: https://doi.org/10.1038/s41467-019-12880" target="doi">015 | https://doi.org/10.1038/s41467-019-12880

4. S. Dittmer, E. King, P. Maaß.
   Singular values for ReLU layers.
   IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.

   online at: https://ieeexplore.ieee.org/document/8891761

5. S. Arridge, P. Maaß, O. Öktem, C. Schönlieb.
   Solving inverse problems using data-driven models.
   Acta Numerica, 28:pp. 1-174, Cambridge University Press, 2019.

   DOI: 10.1017/S0962492919000059

6. T. Kluth, P. Szwargulski, T. Knopp.
   Towards Accurate Modeling of the Multidimensional Magnetic Particle Imaging Physics.
   New Journal of Physics, Article ID 10303 21, 10 pp., 2019.

   online at: https://iopscience.iop.org/article/10.1088/1367-2630/ab4938/pdf

Proceedings (8)

1. 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

2. T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
   Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
   22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
   AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.

   DOI: 10.1063/1.5112634

3. 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

4. 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

5. 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

6. 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

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.

   Spotlight

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

8. T. Kluth, B. Hahn, C. Brandt.
   Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging.
   International Workshop on Magnetic Particle Imaging 2019.
   International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2019, pp. 23-24, Infinite Science Publishing, 2019.

Book Chapters (1)

1. P. Maaß.
   Deep learning for trivial inverse problems.
   Compressed Sensing and its Applications, H. Boche, R. Calderbank, G. Caire, G. Kutyniok, R. Mathar, P. Petersen (Eds.), Applied and Numerical Harmonic Analysis, pp. 195-209, Birkhäuser, 2019.

   DOI: 10.1007/978-3-319-73074-5_6

PhD/Habilitation Thesis (2)

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

2. D. Lantzberg.
   Quantum Frames and Uncertainty Principles arising from Symplectomorphisms.
   Dissertationsschrift, Universität Bremen, 2019.

   online at: http://nbn-resolving.de/urn:nbn:de:gbv:46-00107143-11

Preprints (5)

1. G. Rigaud.
   3D Compton scattering imaging: study of the spectrum and contour reconstruction.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/1908.03066

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

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

3. S. Dittmer, P. Maaß.
   A Projectional Ansatz to Reconstruction.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/1907.04675

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. T. Kluth, B. Jin.
   Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
   Zur Veröffentlichung eingereicht.

Miscellaneous (1)

1. C. Brandt, M. Hamann, J. Leuschner.
   Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
   Berichte aus der Technomathematik 19–01, Universität Bremen, 2019.