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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 parametrized deep assignment flows with random initial conditions. Building on the recent PAC‐Bayes literature and data‐dependent priors, this approach enables (i) to use risk bounds as training objectives for learning posterior...
We introduce the linear assignment flow as an approximation of the full nonlinear assignment flow, which is a method for contextual data labeling on arbitrary graphs. The linear assignment flow is a dynamical system evolving on the tangent space of a statistical manifold. It is numerically determined using exponential integrators and Krylov subspace approximation, for which we provide error estimates...
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