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New research details convergence of Sharpness-Aware Minimization algorithms

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the convergence of Sharpness-Aware Minimization (SAM) algorithms, specifically the gap guided SAM (GSAM) variant. The study theoretically demonstrates that employing increasing batch sizes or decaying learning rates, such as cosine annealing or linear decay, leads to convergence. Numerical comparisons indicate that GSAM with an increasing batch size achieves lower worst-case adaptive sharpness compared to using constant batch sizes and learning rates. AI

IMPACT Provides theoretical and numerical insights into optimizing deep neural network training, potentially improving generalization capabilities.

RANK_REASON Research paper published on arXiv detailing theoretical and numerical findings on optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details convergence of Sharpness-Aware Minimization algorithms

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Research paper published on arXiv detailing theoretical and numerical findings on optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hinata Harada, Hideaki Iiduka ·

    Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate

    arXiv:2409.09984v2 Announce Type: replace Abstract: The sharpness-aware minimization (SAM) algorithm and its variants, including gap guided SAM (GSAM), have been successful at improving the generalization capability of deep neural network models by finding flat local minima of th…

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