A Generalization Bound of Deep Neural Networks for Dependent Data
Quan Huu Do Binh T. Nguyen Lam Si Tung Ho
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
$\phi$-mixing data.