Publications of Dr. Sören Dittmer

Book Chapters (1)

1. P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
   Mathematische Architekturen für Neuronale Netze.
   Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Eds.), Mathematische Semesterberichte, pp. 190-195, Springer Verlag, 2022.

   DOI: 10.1007/s00591-022-00325-y

Articles (9)

1. D. Erzmann, S. Dittmer, H. Harms, P. Maaß.
   DL4TO: A Deep Learning Library for Sample-Efficient Topology Optimization.
   Lecture Notes in Computer Science, Geometric Science of Information. GSI 2023 14071, Springer Verlag, 2023.

   DOI: 10.1007/978-3-031-38271-0_54

2. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
   Invertible residual networks in the context of regularization theory for linear inverse problems.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online at: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

3. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
   Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
   Applied Mathematics for Modern Challenges, 1(2), 2023.

   DOI: 10.3934/ammc.2023007

4. S. Dittmer, M. Roberts, J. Gilbey, A. Biguri, .. AIX-COVNET Collaboration, J. Preller, J. H. F. Rudd, J. A. D. Aston, C. Schönlieb.
   Navigating the development challenges in creating complex data systems.
   nature machine intelligence, 5:681-686, Springer Verlag, 2023.

   DOI: 10.1038/s42256-023-00665-x
   online at: https://www.nature.com/articles/s42256-023-00665-x#citeas

5. T. Shadbahr, M. Roberts, J. Stanczuk, J. Gilbey, P. Teare, S. Dittmer, M. Thorpe, R. V. Torne, E. Sala, P. Lio, M. Patel, .. AIX-COVNET Collaboration, J. H. F. Rudd, T. Mirtti, A. Rannikko, J. A. D. Aston, J. Tang, C. Schönlieb.
   The impact of imputation quality on machine learning classifiers for datasets with missing values.
   Communication medicine, 3, Springer Verlag, 2023.

   DOI: 10.1038/s43856-023-00356-z
   online at: https://www.nature.com/articles/s43856-023-00356-z#citeas

6. S. Dittmer, C. Schönlieb, P. Maaß.
   Ground Truth Free Denoising by Optimal Transport.
   Erscheint in Numerical Algebra, Control, and Optimization

   online at: https://arxiv.org/abs/2007.01575

7. S. Dittmer, T. Kluth, M. Henriksen, P. Maaß.
   Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
   International Journal on Magnetic Particle Imaging, 7(1), 2021.

   online at: https://journal.iwmpi.org/index.php/iwmpi/article/view/148

8. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
   Regularization by architecture: A deep prior approach for inverse problems.
   Journal of Mathematical Imaging and Vision, 62(3):456-470, Springer Verlag, 2020.

   DOI: 10.1007/s10851-019-00923-x
   online at: http://link.springer.com/article/10.1007/s10851-019-00923-x

9. 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

Preprints (9)

1. C. Arndt, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, J. Nickel.
   Bayesian view on the training of invertible residual networks for solving linear inverse problems.
   Zur Veröffentlichung eingereicht.

   online at: https://www.x-mol.net/paper/article/1682514725633245184

2. D. Erzmann, S. Dittmer.
   Equivariant Neural Operators for gradient-Consistent Topology Optimization .
   Zur Veröffentlichung eingereicht.
3. S. Dittmer, M. Roberts, J. Preller, .. AIX-COVNET Collaboration, J. H. F. Rudd, J. A. D. Aston, C. Schönlieb.
   Reinterpreting survival analysis in the universal approximator age.
   Zur Veröffentlichung eingereicht.
4. M. Roberts, A. Hazan, S. Dittmer, J. H. F. Rudd, C. Schönlieb.
   The curious case of the test set AUROC.
   Zur Veröffentlichung eingereicht.
5. S. Dittmer, D. Erzmann, H. Harms, P. Maaß.
   SELTO: Sample-Efficient Learned Topology Optimization.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2209.05098

6. T. Grossmann, S. Dittmer, Y. Korolev, C. Schönlieb.
   Unsupervised Learning of the Total Variation Flow.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2206.04406#

7. S. . Mukherjee, S. Dittmer, Z. . Shumaylov, S. Lunz, O. Öktem, C. Schönlieb.
   Learned convex regularizers for inverse problems.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2008.02839

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

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

9. 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. S. Dittmer.
   On deep learning applied to inverse problems - A chicken-and-egg problem.
   Dissertationsschrift, Universität Bremen, 2020.

Proceedings (1)

1. S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
   A Deep Prior Approach to Magnetic Particle Imaging.
   Machine Learning for Medical Image Reconstruction 2020.
   Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Eds.), pp. 113-122, 2020.

   DOI: 10.1007/978-3-030-61598-7_11