A Generalization Bound of Deep Neural Networks for Dependent Data
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 9 Oct 2023.
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References · 9
- 2017Attention Is All You Needcited by 59,932
- 2017Spectrally-normalized margin bounds for neural networkscited by 681
- 2017Size-Independent Sample Complexity of Neural Networkscited by 289
- 2020Consistent Feature Selection for Analytic Deep Neural Networkscited by 27
- 2022Generalization Bounds via Convex Analysiscited by 27
- 2014Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Datacited by 4
- 2014Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifierscited by 3
- 2021Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Datacited by 2
- 2021Searching for Minimal Optimal Neural Networkscited by 1
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