1 citations · 3 across the 18 of their papers we have counts for
9 papers · 1 filter
Sampling from Boltzmann densities with physics informed low-rank formats
Paul Hagemann, Janina Schütte, David Sommer +2
Our method proposes the efficient generation of samples from an unnormalized Boltzmann density by solving the underlying continuity equation in the low-rank tensor train (TT) forma…
Conditional Generative Models for Contrast-Enhanced Synthesis of T1w and T1 Maps in Brain MRI
Moritz Piening, Fabian Altekrüger, Gabriele Steidl +2
Contrast enhancement by Gadolinium-based contrast agents (GBCAs) is a vital tool for tumor diagnosis in neuroradiology. Based on brain MRI scans of glioblastoma before and after Ga…
Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry
Jannis Chemseddine, Christian Wald, Richard Duong +1
We deal with the task of sampling from an unnormalized Boltzmann density by learning a Boltzmann curve given by energies starting in a simple density . First, we e…
PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
Ségolène Martin, Anne Gagneux, Paul Hagemann +1
In this paper, we introduce Plug-and-Play (PnP) Flow Matching, an algorithm for solving imaging inverse problems. PnP methods leverage the strength of pre-trained denoisers, often…
T-FAKE: Synthesizing Thermal Images for Facial Landmarking
Philipp Flotho, Moritz Piening, Anna Kukleva +1
Facial analysis is a key component in a wide range of applications such as healthcare, autonomous driving, and entertainment. Despite the availability of various facial RGB dataset…
Sparse additive function decompositions facing basis transforms
Fatima Antarou Ba, Oleh Melnyk, Christian Wald +1
High-dimensional real-world systems can often be well characterized by a small number of simultaneous low-complexity interactions. The analysis of variance (ANOVA) decomposition an…
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
Jannis Chemseddine, Paul Hagemann, Gabriele Steidl +1
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While th…
Transfer Operators from Batches of Unpaired Points via Entropic Transport Kernels
Florian Beier, Hancheng Bi, Clément Sarrazin +2
In this paper, we are concerned with estimating the joint probability of random variables and , given independent observation blocks $(\boldsymbol{x}^i,\boldsymbol{y}^i)…
Mixed Noise and Posterior Estimation with Conditional DeepGEM
Paul Hagemann, Johannes Hertrich, Maren Casfor +2
Motivated by indirect measurements and applications from nanometrology with a mixed noise model, we develop a novel algorithm for jointly estimating the posterior and the noise par…