Weak correlations principle explains linearization in gradient-based learning systems

A new paper published on arXiv explores the principle of weak correlations as the underlying reason for the linearization observed in gradient-based learning systems. The research suggests that the simplified dynamics seen in deep learning models, particularly in the infinite limit, can be attributed to weak correlations between the first and higher-order derivatives of the hypothesis function with respect to parameters. This insight is demonstrated in wide neural networks and leads to a derived bound on deviations from linearity during stochastic gradient descent training. AI

IMPACT Provides a theoretical framework for understanding the behavior of deep learning models, potentially guiding future model development.

RANK_REASON Research paper published on arXiv detailing a theoretical principle in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Hugging Face
• Neural tangent kernel
• Ori Shem-Ur
• stochastic gradient descent

• paper
• model release

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