Mathematik, Arbeitsrichtung Angewandte Mathematik

Johannes Hertrich

Technische Universität Berlin
Institut für Mathematik
Sekretariat MA 4-3
Straße des 17. Juni 136
10623 Berlin


Raum MA 477
Tel.: +49 - (0)30 - 314-79758
Email: j.hertrich (at) math.tu-berlin.de
Links: Persönliche Webseite, Google Scholar, Github, Orcid

 

Sekretariat MA 4-3
Julia Wilton
Raum MA 476
Email: wilton_(at) math.tu-berlin.de

Publications

F. Altekrüger, J. Hertrich and G. Steidl (2023).
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels.
International Conference on Machine Learning 2023.
Proceedings of Machine Learning Research, vol. 202, pp. 664-690.
[www], [arxiv], [Code]

F. Altekrüger and J. Hertrich (2023).
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution.
SIAM Journal on Imaging Sciences, vol. 16(3), pp. 1033-1067.
[doi], [arxiv], [Code]

J. Hertrich, C. Wald, F. Altekrüger and P. Hagemann (2023).
Generative Sliced MMD flows with Riesz kernels.
(arXiv Preprint#2305.11463)
[arxiv], [Code]

F. Altekrüger, A. Denker, P. Hagemann, J. Hertrich, P. Maass and G. Steidl (2023).
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
Inverse Problems, vol. 39, no. 6.
[doi], [arxiv], [Code]

J. Hertrich, R. Beinert, M. Gräf and G. Steidl (2023).
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line.
L. Calatroni, M. Donatelli, S. Morigi, M. Prato and M. Santacesaria (eds.)
Scale Space and Variational Methods in Computer Vision.
Lecture Notes in Computer Science, 14009, 431-443.
[doi], [arxiv]

J. Hertrich (2023).
Proximal Residual Flows for Bayesian Inverse Problems.
L. Calatroni, M. Donatelli, S. Morigi, M. Prato and M. Santacesaria (eds.)
Scale Space and Variational Methods in Computer Vision.
Lecture Notes in Computer Science, 14009, 210-222.
[doi], [arxiv], [Code]

G.S. Alberti, J. Hertrich, M. Santacesaria and S. Sciutto (2023).
Manifold Learning by Mixture Models of VAEs for Inverse Problems.
(arXiv Preprint#2303.15244)
[arxiv], [Code]

P. Hagemann, J. Hertrich and G. Steidl (2023).
Generalized Normalizing Flows via Markov Chains.
Elements in Non-local Data Interactions: Foundations and Applications.
Cambridge University Press.
[doi], [arxiv], [Code]

D.P.L. Nguyen, J. Hertrich, J.-F. Aujol and Y. Berthoumieu (2023).
Image super-resolution with PCA reduced generalized Gaussian mixture models.
Inverse Problems and Imaging, vol. 17, pp. 1165-1192.
[doi], [hal]

J. Hertrich, M. Gräf, R. Beinert and G. Steidl (2022).
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels.
(arXiv Preprint#2211.01804)
[arxiv]

P. Hagemann, J. Hertrich and G. Steidl (2022).
Stochastic Normalizing Flows: a Markov Chains Viewpoint.
SIAM/ASA Journal on Uncertainty Quantification, vol. 10, pp. 1162-1190.
[doi], [arxiv], [Code]

J. Hertrich, A. Houdard and C. Redenbach (2022).
Wasserstein Patch Prior for Image Superresolution.
IEEE Transactions on Computational Imaging, vol. 8, pp. 693-704.
[doi], [arxiv], [Code]

J. Hertrich and G. Steidl (2022).
Inertial Stochastic PALM and Applications in Machine Learning.
Sampling Theory, Signal Processing, and Data Analysis, vol. 20, no. 4.
[doi], [arxiv], [Code]

J. Hertrich, D.P.L. Nguyen, J.-F. Aujol, D. Bernard, Y. Berthoumieu, A. Saadaldin and G. Steidl (2022).
PCA reduced Gaussian mixture models with application in superresolution.
Inverse Problems and Imaging, vol. 16, pp. 341-366.
[doi], [arxiv], [Code]

J. Hertrich, F. Ba and G. Steidl (2022).
Sparse Mixture Models Inspired by ANOVA Decompositions.
Electronic Transactions on Numerical Analysis, vol. 55, pp. 142-168.
[doi], [arxiv], [Code]

J. Hertrich, S. Neumayer and G. Steidl (2021).
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms.
Linear Algebra and its Applications, vol 631, pp. 203-234.
[doi], [arxiv], [Code]

M. Hasannasab, J. Hertrich, F. Laus and G. Steidl (2021).
Alternatives to the EM Algorithm for ML-Estimation of Location, Scatter Matrix and Degree of Freedom of the Student-t Distribution.
Numerical Algorithms, vol. 87, pp. 77-118.
[doi], [arxiv], [Code]

T. Batard, J. Hertrich and G. Steidl (2020).
Variational models for color image correction inspired by visual perception and neuroscience.
Journal of Mathematical Imaging and Vision, vol. 62, pp. 1173-1194.
[doi], [hal]

M. Hasannasab, J. Hertrich, S.Neumayer, G. Plonka, S. Setzer and G. Steidl (2020).
Parseval Proximal Neural Networks.
Journal of Fourier Analysis and Applications, vol. 26, no. 59.
[doi], [arxiv], [Code]

M. Bačák, J. Hertrich, S. Neumayer and G. Steidl (2020).
Minimal Lipschitz and ∞-Harmonic Extensions of Vector-Valued Functions on Finite Graphs.
Information and Inference: A Journal of the IMA, vol. 9, pp. 935–959.
[doi], [arxiv], [Code]

J. Hertrich, M. Bačák, S. Neumayer, G. Steidl (2019).
Minimal Lipschitz extensions for vector-valued functions on finite graphs.
M. Burger, J. Lellmann and J. Modersitzki (eds.)
Scale Space and Variational Methods in Computer Vision.
Lecture Notes in Computer Science, 11603, 183-195.
[doi], [Code]

Theses

Proximal Neural Networks and Stochastic Normalizing Flows for Inverse Problems
PhD Thesis, 2023
TU Berlin
[doi]

Superresolution via Student-t Mixture Models
Master Thesis, 2020
TU Kaiserslautern

Infinity Laplacians on Scalar- and Vector-valued Functions and Optimal Lipschitz Extensions on Graphs
Bachelor Thesis, 2018
TU Kaiserslautern