AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Ensemble Diversity Optimization (EDO), a prediction-space framework that jointly optimizes ensemble weights, effective cardinality, and calibration through a unified differentiable objective, provides an efficient, model-agnostic approach for modeling human subjectivity in supervised learning.
SETTA (Spectral-Energy Test-Time Adaptation), a prediction-level graph test-time adaptation framework that refines frozen outputs without test labels, gradients, parameter updates, or learnable adaptation parameters, is presented.
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