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Self-certifying classification by linearized deep assignment

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-...

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Hauptverfasser: Boll, Bastian (VerfasserIn) , Zeilmann, Alexander (VerfasserIn) , Petra, Stefania (VerfasserIn) , Schnörr, Christoph (VerfasserIn)
Dokumenttyp: Article (Journal) Kapitel/Artikel
Sprache:Englisch
Veröffentlicht: 18 Feb 2022
Ausgabe:Version v2
In: Arxiv
Year: 2022, Pages: 1-19
DOI:10.48550/arXiv.2201.11162
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.48550/arXiv.2201.11162
Verlag, kostenfrei, Volltext: http://arxiv.org/abs/2201.11162
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Verfasserangaben:Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schnörr

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