# On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data

arXiv: https://arxiv.org/abs/2011.00328 · HTML: https://mlsanity.com/paper/2011.00328/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 14 of 14)

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- [Excess risk bound for deep learning under weak dependence](https://mlsanity.com/paper/2302.07503/) (2023)
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- [Robust deep learning from weakly dependent data](https://mlsanity.com/paper/2405.05081/) (2024)
- [Approximation Bounds for Recurrent Neural Networks with Application to Regression](https://mlsanity.com/paper/2409.05577/) (2024)
- [Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds](https://mlsanity.com/paper/2410.21702/) (2024)
- [Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality](https://mlsanity.com/paper/2406.08321/) (2024)
- [Deep learning for psi-weakly dependent processes](https://mlsanity.com/paper/2302.00333/) (2023)
- [Penalized deep neural networks estimator with general loss functions under weak dependence](https://mlsanity.com/paper/2305.06230/) (2023)
- [A general framework for deep learning](https://mlsanity.com/paper/2512.23425/) (2025)
- [A Subsampling Based Neural Network for Spatial Data](https://mlsanity.com/paper/2411.03620/) (2024)
- [Learning Causal States Under Partial Observability and Perturbation](https://mlsanity.com/paper/2512.00357/) (2025)
- [Deep regression learning from dependent observations with minimum error entropy principle](https://mlsanity.com/paper/2603.11138/) (2026)
- [Adaptive deep nonparametric regression from dependent data under covariate shift](https://mlsanity.com/paper/2607.20309/) (2026)

## Abstract (arXiv)

A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumptions on the regression function a deep recurrent neural network estimate is able to circumvent the curse of dimensionality.