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.

翻译：现有的深度神经网络泛化界要求数据独立同分布（iid），但在进化生物学、传染病流行病学以及股价预测等实际应用中，这一假设可能不成立。本文建立了前馈神经网络对非平稳$\phi$-混合数据的泛化界。