A Generalization Bound of Deep Neural Networks for Dependent Data

Quan Huu Do, Binh T. Nguyen, Lam Si Tung Ho
10/9/2023
stat.MLcs.LG

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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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Cite this paper

@article{do2023generalization,
  title  = {A Generalization Bound of Deep Neural Networks for Dependent Data},
  author = {Quan Huu Do and Binh T. Nguyen and Lam Si Tung Ho},
  year   = {2023},
  eprint = {2310.05892},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.05892v1}
}

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