AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An algorithm-agnostic, information-theoretic approach based on the PAC-Bayes framework is introduced that quantifies the potential of privileged features before committing to a model, and takes a necessary step toward an information-theoretic understanding of LUPI.
The results demonstrate a rigorous learning separation for a natural ML task based on Hamiltonian evolution, while building connections between quantum learning theory, quantum simulation, and QML.
It is shown that gradient-based PQCs can exhibit improved performance on unseen data as model size increases, displaying the phenomenon of double descent, which contrasts with the traditional view that larger models lead to degraded generalization.
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