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Hiroaki Aizawa

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#machine learning Preprint Aug 2026

Temperature-Adaptive Transformed Teacher Matching

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.

Hiroaki Aizawa, Yoshikazu Hayashi · 0 citations