# Early stopping by correlating online indicators in neural networks

arXiv: https://arxiv.org/abs/2402.02513 · HTML: https://mlsanity.com/paper/2402.02513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 134 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Early stopping and non-parametric regression: An optimal data-dependent stopping rule](https://mlsanity.com/paper/1306.3574/) (2013)
- [Optimal Rates For Regularization Of Statistical Inverse Learning Problems](https://mlsanity.com/paper/1604.04054/) (2016)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [Early stopping for kernel boosting algorithms: A general analysis with localized complexities](https://mlsanity.com/paper/1707.01543/) (2017)
- [Deep Contextualized Self-training for Low Resource Dependency Parsing](https://mlsanity.com/paper/1911.04286/) (2019)
- [A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping](https://mlsanity.com/paper/1409.5165/) (2014)
- [Hierarchical Pointer Net Parsing](https://mlsanity.com/paper/1908.11571/) (2019)
- [Neural Probabilistic Model for Non-projective MST Parsing](https://mlsanity.com/paper/1701.00874/) (2017)

## Abstract (arXiv)

In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus improving the predictive power of that type of modeling. Our proposal exploits the correlation over time in a collection of online indicators, namely characteristic functions for indicating if a set of hypotheses are met, associated with a range of independent stopping conditions built from a canary judgment to evaluate the presence of overfitting. That way, we provide a formal basis for decision making in terms of interrupting the learning process. As opposed to previous approaches focused on a single criterion, we take advantage of subsidiarities between independent assessments, thus seeking both a wider operating range and greater diagnostic reliability. With a view to illustrating the effectiveness of the halting condition described, we choose to work in the sphere of natural language processing, an operational continuum increasingly based on machine learning. As a case study, we focus on parser generation, one of the most demanding and complex tasks in the domain. The selection of cross-validation as a canary function enables an actual comparison with the most representative early stopping conditions based on overfitting identification, pointing to a promising start toward an optimal bias and variance control.