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TU Berlin

Inhalt des Dokuments

Johannes Hertrich

Lupe

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:

 

Sekretariat MA 4-3
Julia Wilton
Raum MA 476
Email:

Publications

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, P. Hagemann and G. Steidl (2021).
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains.
(arXiv Preprint#2111.12506)
[arxiv]

J. Hertrich, A. Houdard and C. Redenbach (2021).
Wasserstein Patch Prior for Image Superresolution.
(arXiv Preprint#2109.12880)
[arxiv]

P. Hagemann, J. Hertrich and G. Steidl (2021).
Stochastic Normalizing Flows: a Markov Chains Viewpoint.
(arXiv Preprint#2109.11375)
[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]

J. Hertrich, D. P. L. Nguyen, J.-F. Aujol, D. Bernard, Y. Berthoumieu, A. Saadaldin and G. Steidl (2021).
PCA reduced Gaussian mixture models with application in superresolution.
Inverse Problems and Imaging.
[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]

J. Hertrich and G. Steidl (2020).
Inertial Stochastic PALM (iSPALM) and Applications in Machine Learning.
(arXiv Preprint#2005.02204)
[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]

Thesis

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

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

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