A generalization bound of deep neural networks for dependent data

Quan Huu Do, Binh T. Nguyen and Lam Si Tung Ho

Statistics & Probability Letters, 2024, vol. 208, issue C

Abstract: Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock price prediction. This work establishes a generalization bound of feed-forward neural networks for non-stationary φ-mixing data.

Keywords: Neural networks; Generalization bound; Non-stationary process; Mixing stochastic process (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.spl.2024.110060

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