Web: http://arxiv.org/abs/2201.11162

Jan. 28, 2022, 2:10 a.m. | Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schnörr

cs.LG updates on arXiv.org arxiv.org

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 distributions on the hypothesis space and (ii) to compute
tight out-of-sample risk certificates of randomized classifiers more
efficiently than related work. Comparison with …

arxiv classification deep ml

More from arxiv.org / cs.LG updates on arXiv.org

Data Analytics and Technical support Lead

@ Coupa Software, Inc. | Bogota, Colombia

Data Science Manager

@ Vectra | San Jose, CA

Data Analyst Sr

@ Capco | Brazil - Sao Paulo

Data Scientist (NLP)

@ Builder.ai | London, England, United Kingdom - Remote

Senior Data Analyst

@ BuildZoom | Scottsdale, AZ/ San Francisco, CA/ Remote

Senior Research Scientist, Speech Recognition

@ SoundHound Inc. | Toronto, Canada