Clemens Arndt

Zeitschriftenartikel

C. Arndt.
Regularization Theory of the Analytic Deep Prior Approach.
Inverse Problems, 38(11), 2022.
DOI: 10.1088/1361-6420/ac9011

C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, G. Rigaud.
In Focus - hybrid deep learning approaches to the HDC2021 challenge.
Inverse Problems and Imaging,  2022.
DOI: 10.3934/ipi.2022061

Preprints

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.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2306.01335

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