The assignment flow recently introduced in the J. Math. Imaging and Vision 58/2 (2017)
constitutes a high-dimensional dynamical system that evolves on a statistical product …

We propose a novel class of deep stochastic predictors for classifying metric data on graphs
within the PAC-Bayes risk certification paradigm. Classifiers are realized as linearly …

This paper introduces a novel nonlocal partial difference equation (G-PDE) for labeling
metric data on graphs. The G-PDE is derived as a nonlocal reparametrization of the …

We introduce a novel algorithm for estimating optimal parameters of linearized assignment
flows for image labeling. An exact formula is derived for the parameter gradient of any loss …

This paper introduces a novel approach to uncertainty quantification of image labelings
determined by assignment flows. Local uncertainties caused by ambiguous data and noise …

We propose a novel class of deep stochastic predictors for classifying metric data on graphs
within the PAC‐Bayes risk certification paradigm. Classifiers are realized as linearly …

In this thesis, we focus on the image labeling problem which is the task of performing unique
pixel-wise label decisions to simplify the image while reducing its redundant information. We …