A Generalization Bound of Deep Neural Networks for Dependent Data

Quan Huu Do; Binh T. Nguyen; Lam Si Tung Ho

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arXiv · 2310.05892

A Generalization Bound of Deep Neural Networks for Dependent Data

Quan Huu Do·Binh T. Nguyen·Lam Si Tung Ho

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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.

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Quan Huu Do, Binh T. Nguyen, Lam Si Tung Ho. 2023-10-09. A Generalization Bound of Deep Neural Networks for Dependent Data. https://arxiv.org/abs/2310.05892

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