Johannes Hertrich

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 35

Top Topics

Image Reconstruction

Inverse Problems

Top Venues

Numer. Algorithms

Publications

Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann
Generative Sliced MMD Flows with Riesz Kernels. ICLR (2024)
Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
Mixed Noise and Posterior Estimation with Conditional DeepGEM. CoRR (2024)
Johannes Hertrich
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms. CoRR (2024)
Johannes Hertrich, Robert Gruhlke
Importance Corrected Neural JKO Sampling. CoRR (2024)
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel. ICLR (2024)
Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
Mixed noise and posterior estimation with conditional deepGEM. Mach. Learn. Sci. Technol. 5 (3) (2024)
Johannes Hertrich, Robert Beinert, Manuel Gräf, Gabriele Steidl
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line. CoRR (2023)
Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. ICML (2023)
Johannes Hertrich
Proximal Residual Flows for Bayesian Inverse Problems. SSVM (2023)
Fabian Altekrüger, Johannes Hertrich
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution. SIAM J. Imaging Sci. 16 (3) (2023)
Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto
Manifold Learning by Mixture Models of VAEs for Inverse Problems. CoRR (2023)
Johannes Hertrich, Robert Beinert, Manuel Gräf, Gabriele Steidl
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line. SSVM (2023)
Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels. CoRR (2023)
Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann
Generative Sliced MMD Flows with Riesz Kernels. CoRR (2023)
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel. CoRR (2023)
Moritz Piening, Fabian Altekrüger, Johannes Hertrich, Paul Hagemann, Andrea Walther, Gabriele Steidl
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction. CoRR (2023)
Fabian Altekrüger, Johannes Hertrich
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution. CoRR (2022)
Johannes Hertrich
Proximal Residual Flows for Bayesian Inverse Problems. CoRR (2022)
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint. SIAM/ASA J. Uncertain. Quantification 10 (1) (2022)
Johannes Hertrich, Antoine Houdard, Claudia Redenbach
Wasserstein Patch Prior for Image Superresolution. IEEE Trans. Computational Imaging 8 (2022)
Johannes Hertrich, Manuel Gräf, Robert Beinert, Gabriele Steidl
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels. CoRR (2022)
Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization. CoRR (2022)
Johannes Hertrich, Antoine Houdard, Claudia Redenbach
Wasserstein Patch Prior for Image Superresolution. CoRR (2021)
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint. CoRR (2021)
Johannes Hertrich, Paul Hagemann, Gabriele Steidl
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains. CoRR (2021)
Marzieh Hasannasab, Johannes Hertrich, Friederike Laus, Gabriele Steidl
Correction to: Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution. Numer. Algorithms 88 (1) (2021)
Marzieh Hasannasab, Johannes Hertrich, Friederike Laus, Gabriele Steidl
Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution. Numer. Algorithms 87 (1) (2021)
Johannes Hertrich, Sebastian Neumayer, Gabriele Steidl
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms. CoRR (2020)
Johannes Hertrich, Gabriele Steidl
Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models. CoRR (2020)
Thomas Batard, Johannes Hertrich, Gabriele Steidl
Variational Models for Color Image Correction Inspired by Visual Perception and Neuroscience. J. Math. Imaging Vis. 62 (9) (2020)
Johannes Hertrich, Dang Phoung Lan Nguyen, Jean-François Aujol, Dominique Bernard, Yannick Berthoumieu, Abdellativ Sa'adaldin, Gabriele Steidl
PCA Reduced Gaussian Mixture Models with Applications in Superresolution. CoRR (2020)
Johannes Hertrich, Miroslav Bacák, Sebastian Neumayer, Gabriele Steidl
Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs. SSVM (2019)
Johannes Hertrich
Infinity-Laplacians on Scalar- and Vector-Valued Functions and Optimal Lipschitz Extensions on Graphs. CoRR (2019)
Marzieh Hasannasab, Johannes Hertrich, Friederike Laus, Gabriele Steidl
Alternatives of the EM Algorithm for Estimating the Parameters of the Student-t Distribution. CoRR (2019)
Marzieh Hasannasab, Johannes Hertrich, Sebastian Neumayer, Gerlind Plonka, Simon Setzer, Gabriele Steidl
Parseval Proximal Neural Networks. CoRR (2019)