This paper studies a meta-simplex concept and geometric embedding framework for multi-
population replicator dynamics. Central results are two embedding theorems which …

Assignment flows comprise basic dynamical systems for modeling data labeling and related
machine learning tasks in supervised and unsupervised scenarios. They provide adaptive …

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 …

Assignment flows denote a class of dynamical models for contextual data labelling
(classification) on graphs. We derive a novel parametrisation of assignment flows that …

We study the inverse problem of model parameter learning for pixelwise image labeling,
using the linear assignment flow and training data with ground truth. This is accomplished by …

This paper extends the recently introduced assignment flow approach for supervised image
labeling to unsupervised scenarios where no labels are given. The resulting self-assignment …

At the present time optical coherence tomography (OCT) is among the most commonly used
non-invasive imaging methods for the acquisition of large volumetric scans of human retinal …

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 the unsupervised assignment flow that couples the assignment flow
for supervised image labeling (Åström et al. in J Math Imaging Vis 58 (2): 211–238, 2017) …

This paper introduces assignment flows for density matrices as state spaces for
representation and analysis of data associated with vertices of an underlying weighted …