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 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 …

Structured prediction is the task of jointly predicting realizations of multiple coupled random
variables. This statistical problem is central to many advanced applications of deep learning …

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 …