A generalization bound of deep neural networks for dependent data
STATISTICS & PROBABILITY LETTERS(2024)
摘要
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.
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关键词
Neural networks,Generalization bound,Non-stationary process,Mixing stochastic process
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