Random Matrix Theory framework extends analysis for deep learning models

This paper introduces a new framework called High-dimensional Equivalent, extending Random Matrix Theory (RMT) to analyze nonlinear machine learning models like Deep Neural Networks (DNNs). The framework addresses challenges posed by high dimensionality, nonlinearity, and the analysis of generic eigenspectral functionals in overparameterized models. The research provides precise characterizations of training and generalization performance for various network types, capturing phenomena such as scaling laws, double descent, and nonlinear learning dynamics. AI

IMPACT Provides a unified theoretical perspective for understanding deep learning in high-dimensional, overparameterized settings.

RANK_REASON The item is an academic paper published on arXiv detailing a new theoretical framework for analyzing deep learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• deep learning
• Deep Neural Networks
• High-dimensional Equivalent
• machine learning
• Random Matrix Theory
• Zhenyu Liao

• paper
• model release

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