Publikationen der AG Technomathematik

Zeitschriftenartikel (10)

1. F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
   PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
   Inverse Problems, 39(6), 2023.

   online unter: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

2. D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenkötter, A. Rademacher, U. Iben, P. Maaß.
   Deep learning methods for partial differential equations and related parameter identification problems.
   Inverse Problems, 39(10), 2023.

   DOI: 10.1088/1361-6420/ace9d4

3. 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

4. J. Gödeke, G. Rigaud.
   Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods.
   Inverse Problems, 39(3), 2023.

   DOI: 10.1088/1361-6420/acb2ed

5. 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 unter: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

6. 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

7. 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 unter: https://www.nature.com/articles/s42256-023-00665-x#citeas

8. A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
   Score-Based Generative Models for PET Image Reconstruction.
   Erscheint in Machine Learning for Biomedical Imaging

   online unter: https://arxiv.org/abs/2308.14190

9. 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 unter: https://www.nature.com/articles/s43856-023-00356-z#citeas

10. J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
    Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
    Transactions on Machine Learning Research, 12, 2023.

    online unter: https://openreview.net/forum?id=FWyabz82fH

Tagungsbeiträge (1)

1. M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß.
   SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
   Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.
   

   online unter: https://2023.midl.io/papers/p014

Qualifikationsarbeiten (3)

1. J. Leuschner.
   Deep Learning for Computed Tomography Reconstruction: Learned Methods, Deep Image Prior, and Uncertaninty Estimation.
   Dissertationsschrift, Universität Bremen, 2023.

   DOI: 10.26092/elib/2704

2. D. Nganyu Tanyu.
   On the Interplay between Deep Learning Partial Differential Equations and Inverse Problems.
   Dissertationsschrift, Universität Bremen, 2023.
3. P. Gralla.
   Tikhonov Functionals Incorporating Tolerances in Discrepancy Term for Inverse Problems.
   Dissertationsschrift, Universität Bremen, 2023.

   DOI: 10.26092/elib/2097

Preprints (12)

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

2. C. Brandt, T. Kluth, T. Knopp, L. Westen.
   Dynamic image reconstruction with motion priors in application to 3d magnetic particle imaging.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2306.11625

3. D. Nganyu Tanyu, J. Ning, A. Hauptmann, B. Jin, P. Maaß.
   Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2310.18636

4. M. Beckmann, N. Heilenkötter.
   Equivariant Neural Networks for Indirect Measurements.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2306.16506

5. D. Erzmann, S. Dittmer.
   Equivariant Neural Operators for gradient-Consistent Topology Optimization .
   Zur Veröffentlichung eingereicht.
6. M. Beckmann, A. Bhandari, M. Iske.
   Fourier-Domain Inversion for the Modulo Radon Transform.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2307.13114

7. R. Barbano, J. Antorán, J. Leuschner, J. M. Hernández-Lobato, B. Jin, Z. Kereta.
   Image Reconstruction via Deep Image Prior Subspaces.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2302.10279

8. T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
   Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
   Zur Veröffentlichung eingereicht.

   online unter: https://aps.arxiv.org/abs/2307.10474

9. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2302.02181

10. 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.
11. M. Roberts, A. Hazan, S. Dittmer, J. H. F. Rudd, C. Schönlieb.
    The curious case of the test set AUROC.
    Zur Veröffentlichung eingereicht.
12. H. Albers, T. Kluth.
    Time-dependent parameter identification in a Fokker-Planck equation based magnetization model of large ensembles of nanoparticles.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2307.03560