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Polyak and Nesterov momentum methods analyzed for large-batch ML training

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the impact of momentum on large-batch training for machine learning models. The study characterizes risk stability by defining a critical learning rate for stable training, with specific formulas derived for SGD, Polyak, and Nesterov momentum methods. The findings reveal how momentum's role shifts with batch size, showing that Polyak's method increases the critical batch size for better parallelism without losing data efficiency, while Nesterov's method reduces noise accumulation for improved data efficiency in large-batch scenarios. AI

IMPACT Provides theoretical insights into optimizing large-batch training, potentially leading to more efficient model development.

RANK_REASON Academic paper detailing theoretical advancements in machine learning training algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

Polyak and Nesterov momentum methods analyzed for large-batch ML training

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Academic paper detailing theoretical advancements in machine learning training algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jia-Nan Wang, Zixun Huang, Kairui Li, Lei Wu ·

    Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency

    arXiv:2609.02728v1 Announce Type: new Abstract: We study when and how momentum improves large-batch training in the one-pass regime, using power-law kernel regression as a tractable setting. We first characterize risk stability through the critical learning rate, defined as the l…

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