AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Sparsity-Adaptive Sharpness-Aware Minimization (SA-SAM), which derives a sparsity-dependent SAM/ASAM perturbation radius by keeping the mean absolute perturbation approximately invariant as sparsity increases, is introduced.
This paper introduces a sample-wise inverse-temperature update for TTM by locally minimizing the Kullback-Leibler divergence between the temperature-scaled teacher distribution and the student's prediction and derives closed-form first and second derivatives with respect to the inverse temperature.
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