# A Generalization Bound of Deep Neural Networks for Dependent Data

arXiv: https://arxiv.org/abs/2310.05892 · HTML: https://mlsanity.com/paper/2310.05892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Spectrally-normalized margin bounds for neural networks](https://mlsanity.com/paper/1706.08498/) (2017)
- [Size-Independent Sample Complexity of Neural Networks](https://mlsanity.com/paper/1712.06541/) (2017)
- [Consistent Feature Selection for Analytic Deep Neural Networks](https://mlsanity.com/paper/2010.08097/) (2020)
- [Generalization Bounds via Convex Analysis](https://mlsanity.com/paper/2202.04985/) (2022)
- [Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data](https://mlsanity.com/paper/1406.3166/) (2014)
- [Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers](https://mlsanity.com/paper/1408.2714/) (2014)
- [Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data](https://mlsanity.com/paper/2108.10825/) (2021)
- [Searching for Minimal Optimal Neural Networks](https://mlsanity.com/paper/2109.13061/) (2021)

## 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.