Deep learning's depth advantage explained by state-transition model

A new research paper explores the theoretical underpinnings of why deep learning models often outperform shallower ones. The study introduces an implementation-agnostic state-transition model to analyze generalization bounds, separating approximation error from statistical complexity. It identifies specific geometric and semigroup mechanisms that contribute to depth's advantage, suggesting that depth is statistically beneficial when approximation improves rapidly while the transition semigroup remains geometrically tame. AI

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IMPACT Provides theoretical insights into the benefits of deep neural network architectures.

RANK_REASON This is a theoretical computer science paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Sho Sonoda

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