Nick Heilenkötter

Publications (Selection)complete list

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. 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. 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

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

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

5. J. Le Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner , J. Schaller , P. Maaß.
   Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma.
   MDPI Journal of Imaging, 71 7(4), Meisenbach Verlag, Bamberg, 2021.

   DOI: 10.3390/jimaging7040071