Vanilla SGD with Momentum Analyzed for Heavy-Tailed Noise

Researchers have analyzed the convergence properties of vanilla Stochastic Gradient Descent (SGD) with momentum when subjected to heavy-tailed noise. Their findings indicate that while vanilla SGD with momentum can handle such noise without explicit gradient clipping or normalization, its convergence rates are suboptimal compared to modified SGD variants. The study provides theoretical convergence analyses for various objective functions, demonstrating inherent limitations of vanilla methods in these noisy conditions, with experimental results supporting the theoretical conclusions. AI

IMPACT Provides theoretical insights into optimization methods crucial for training large AI models.

RANK_REASON Academic paper analyzing optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

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