New research outlines SGD preconditioner design for stability and noise reduction

A new research paper published on arXiv details design criteria for stochastic gradient descent (SGD) preconditioners, focusing on local conditioning, noise floors, and basin stability. The paper derives bounds where convergence rate and noise floor are influenced by a symmetric positive definite matrix M. For nonconvex objectives, it establishes a preconditioner-dependent basin-stability guarantee, particularly relevant for Scientific Machine Learning (SciML) applications where physical fidelity and numerical stability are crucial. Experiments on diagnostic and SciML benchmarks validate the proposed design principle of choosing M to improve local conditioning while reducing noise. AI

IMPACT Provides theoretical insights into optimizing machine learning model training, potentially leading to more stable and efficient learning processes.

RANK_REASON This is a research paper published on arXiv detailing theoretical and experimental findings in optimization algorithms for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Hugging Face
• Mitchell Scott
• Scientific Machine Learning
• SciML
• SGD
• stochastic gradient descent

• paper
• other

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