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ORCIDJohannes Hertrich
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2020 – today
- 2024
[j8]Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto:
Manifold Learning by Mixture Models of VAEs for Inverse Problems. J. Mach. Learn. Res. 25: 202:1-202:35 (2024)- [j7]Paul Hagemann
, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl:
Mixed noise and posterior estimation with conditional deepGEM. Mach. Learn. Sci. Technol. 5(3): 35001 (2024) - [c6]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 - [c5]Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann:
Generative Sliced MMD Flows with Riesz Kernels. ICLR 2024 - [i23]Johannes Hertrich:
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms. CoRR abs/2401.08260 (2024) - [i22]Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl:
Mixed Noise and Posterior Estimation with Conditional DeepGEM. CoRR abs/2402.02964 (2024) - [i21]Johannes Hertrich, Robert Gruhlke:
Importance Corrected Neural JKO Sampling. CoRR abs/2407.20444 (2024) - [i20]Johannes Hertrich, Tim Jahn, Michael Quellmalz:
Fast Summation of Radial Kernels via QMC Slicing. CoRR abs/2410.01316 (2024) - 2023
- [b1]Johannes Hertrich:
Proximal neural networks and stochastic normalizing flows for inverse problems. Technical University of Berlin, Germany, 2023 - [j6]Fabian Altekrüger, Johannes Hertrich:
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution. SIAM J. Imaging Sci. 16(3): 1033-1067 (2023) - [c4]Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl:
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. ICML 2023: 664-690 - [c3]Johannes Hertrich:
Proximal Residual Flows for Bayesian Inverse Problems. SSVM 2023: 210-222 - [c2]Johannes Hertrich, Robert Beinert, Manuel Gräf, Gabriele Steidl:
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line. SSVM 2023: 431-443 - [i19]Johannes Hertrich, Robert Beinert, Manuel Gräf, Gabriele Steidl:
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line. CoRR abs/2301.04441 (2023) - [i18]Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl:
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels. CoRR abs/2301.11624 (2023) - [i17]Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto:
Manifold Learning by Mixture Models of VAEs for Inverse Problems. CoRR abs/2303.15244 (2023) - [i16]Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann:
Generative Sliced MMD Flows with Riesz Kernels. CoRR abs/2305.11463 (2023) - [i15]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 abs/2310.03054 (2023) - [i14]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 abs/2312.16611 (2023) - 2022
- [j5]Paul Hagemann, Johannes Hertrich, Gabriele Steidl:
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint. SIAM/ASA J. Uncertain. Quantification 10(1): 1162-1190 (2022) - [j4]Johannes Hertrich, Antoine Houdard, Claudia Redenbach:
Wasserstein Patch Prior for Image Superresolution. IEEE Trans. Computational Imaging 8: 693-704 (2022) - [i13]Fabian Altekrüger, Johannes Hertrich:
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution. CoRR abs/2201.08157 (2022) - [i12]Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl:
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization. CoRR abs/2205.12021 (2022) - [i11]Johannes Hertrich, Manuel Gräf, Robert Beinert, Gabriele Steidl:
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels. CoRR abs/2211.01804 (2022) - [i10]Johannes Hertrich:
Proximal Residual Flows for Bayesian Inverse Problems. CoRR abs/2211.17158 (2022) - 2021
- [j3]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): 77-118 (2021) - [j2]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): 521-522 (2021) - [i9]Paul Hagemann, Johannes Hertrich, Gabriele Steidl:
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint. CoRR abs/2109.11375 (2021) - [i8]Johannes Hertrich, Antoine Houdard, Claudia Redenbach:
Wasserstein Patch Prior for Image Superresolution. CoRR abs/2109.12880 (2021) - [i7]Johannes Hertrich, Paul Hagemann, Gabriele Steidl:
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains. CoRR abs/2111.12506 (2021) - 2020
- [j1]Thomas Batard, Johannes Hertrich, Gabriele Steidl:
Variational Models for Color Image Correction Inspired by Visual Perception and Neuroscience. J. Math. Imaging Vis. 62(9): 1173-1194 (2020) - [i6]Johannes Hertrich, Gabriele Steidl:
Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models. CoRR abs/2005.02204 (2020) - [i5]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 abs/2009.07520 (2020) - [i4]Johannes Hertrich, Sebastian Neumayer, Gabriele Steidl:
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms. CoRR abs/2011.02281 (2020)
2010 – 2019
- 2019
- [c1]Johannes Hertrich, Miroslav Bacák, Sebastian Neumayer, Gabriele Steidl:
Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs. SSVM 2019: 183-195 - [i3]Marzieh Hasannasab, Johannes Hertrich, Friederike Laus, Gabriele Steidl:
Alternatives of the EM Algorithm for Estimating the Parameters of the Student-t Distribution. CoRR abs/1910.06623 (2019) - [i2]Johannes Hertrich:
Infinity-Laplacians on Scalar- and Vector-Valued Functions and Optimal Lipschitz Extensions on Graphs. CoRR abs/1910.13805 (2019) - [i1]Marzieh Hasannasab, Johannes Hertrich, Sebastian Neumayer, Gerlind Plonka, Simon Setzer, Gabriele Steidl:
Parseval Proximal Neural Networks. CoRR abs/1912.10480 (2019)
Coauthor Index
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last updated on 2024-11-11 21:28 CET by the dblp team
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