The solution of inverse problems is of fundamental interest in medical and astronomical
imaging, geophysics as well as engineering and life sciences. Recent advances were made …

We propose conditional flows of the maximum mean discrepancy (MMD) with the negative
distance kernel for posterior sampling and conditional generative modeling. This MMD …

Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful
generative models. This Element provides a unified framework to handle these approaches …

In order to sample from an unnormalized probability density function, we propose to
combine continuous normalizing flows (CNFs) with rejection-resampling steps based on …

Conditional generative models became a very powerful tool to sample from Bayesian
inverse problem posteriors. It is well-known in classical Bayesian literature that posterior …

This paper provides an overview of current approaches for solving inverse problems in
imaging using variational methods and machine learning. A special focus lies on point …

Learning neural networks using only few available information is an important ongoing
research topic with tremendous potential for applications. In this paper, we introduce a …

Scientific imaging problems are often severely ill-posed, and hence have significant intrinsic
uncertainty. Accurately quantifying the uncertainty in the solutions to such problems is …

In this paper, we build on optimal transport (OT) theory to present a novel asymmetrically
unbalanced variant, the semi-unbalanced optimal transport (SUOT), specifically designed …

Super-resolution (SR) is an ill-posed inverse problem which consists in proposing high-
resolution images consistent with a given low-resolution one. While most SR algorithms are …