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Masayuki Tanaka

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Conference Open access Aug 2026

EMASAM: a Computationally Efficient Sharpness-Aware Minimization via EMA-Guided Perturbations

Recent progress in optimization research has highlighted the sharpness of the loss landscape as a key factor in narrowing the generalization gap. Motivated by this insight, Sharpness-Aware Minimization (SAM) was proposed as a training strategy that enhances generalization. Despite the promising performance, SAM suffers...

Tanapat Ratchatorn, Masayuki Tanaka · 0 citations

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