Publikationen der AG Technomathematik

Zeitschriftenartikel (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 unter: 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 unter: 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 unter: https://iopscience.iop.org/article/10.1088/1367-2630/ab4938/pdf

Tagungsbeiträge (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 unter: 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 unter: 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 unter: 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 unter: 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 unter: 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, S. 9916--9926, 2019.

   Spotlight

   online unter: 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, S. 23-24, Infinite Science Publishing, 2019.

Buchkapitel (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 (Hrsg.), Applied and Numerical Harmonic Analysis, S. 195-209, Birkhäuser, 2019.

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

Qualifikationsarbeiten (2)

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

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

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

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

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

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

   online unter: 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 unter: 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.

Sonstiges (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.