Johannes Hertrich

Maren Casfor

Sebastian Heidenreich

Mach. Learn. Sci. Technol., 2024

Manifold Learning by Mixture Models of VAEs for Inverse Problems.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

Plug-and-Play Half-Quadratic Splitting for Ptychography.

[BibT_eX]

[DOI]

CoRR, 2024

Generative Feature Training of Thin 2-Layer Networks.

[BibT_eX]

[DOI]

Sebastian Neumayer

CoRR, 2024

Fast Summation of Radial Kernels via QMC Slicing.

[BibT_eX]

[DOI]

Tim Jahn

Michael Quellmalz

CoRR, 2024

Importance Corrected Neural JKO Sampling.

[BibT_eX]

[DOI]

Robert Gruhlke

CoRR, 2024

Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms.

[BibT_eX]

[DOI]

CoRR, 2024

Generative Sliced MMD Flows with Riesz Kernels.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution.

[BibT_eX]

[DOI]

SIAM J. Imaging Sci., September, 2023

Proximal neural networks and stochastic normalizing flows for inverse problems.

[BibT_eX]

[DOI]

PhD thesis, 2023

Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction.

[BibT_eX]

[DOI]

CoRR, 2023

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels.

[BibT_eX]

[DOI]

CoRR, 2023

Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line.

[BibT_eX]

[DOI]

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023

Proximal Residual Flows for Bayesian Inverse Problems.

[BibT_eX]

[DOI]

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023

Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint.

[BibT_eX]

[DOI]

Paul Hagemann

SIAM/ASA J. Uncertain. Quantification, March, 2022

Wasserstein Patch Prior for Image Superresolution.

[BibT_eX]

[DOI]

Antoine Houdard

Claudia Redenbach

IEEE Trans. Computational Imaging, 2022

Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels.

[BibT_eX]

[DOI]

CoRR, 2022

PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization.

[BibT_eX]

[DOI]

CoRR, 2022

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Correction to: Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution.

[BibT_eX]

[DOI]

Numer. Algorithms, 2021

Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution.

[BibT_eX]

[DOI]

Numer. Algorithms, 2021

A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains.

[BibT_eX]

[DOI]

Paul Hagemann

CoRR, 2021

2020

Variational Models for Color Image Correction Inspired by Visual Perception and Neuroscience.

[BibT_eX]

[DOI]

Thomas Batard

J. Math. Imaging Vis., 2020

Convolutional Proximal Neural Networks and Plug-and-Play Algorithms.

[BibT_eX]

[DOI]

Sebastian Neumayer

CoRR, 2020

PCA Reduced Gaussian Mixture Models with Applications in Superresolution.

[BibT_eX]

[DOI]

Dang Phoung Lan Nguyen

Jean-François Aujol

Dominique Bernard

Yannick Berthoumieu

Abdellativ Sa'adaldin

CoRR, 2020

Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Parseval Proximal Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Infinity-Laplacians on Scalar- and Vector-Valued Functions and Optimal Lipschitz Extensions on Graphs.

[BibT_eX]

[DOI]