Summary of Early Stopping by Correlating Online Indicators in Neural Networks, By Manuel Vilares Ferro et al.
Early stopping by correlating online indicators in neural networks
by Manuel Vilares Ferro, Yerai Doval Mosquera, Francisco J. Ribadas Pena, Victor M. Darriba Bilbao
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel technique to identify overfitting phenomena during neural network training, enabling reliable and trustworthy early stopping conditions that improve predictive power. The approach exploits correlation over time in online indicators, such as characteristic functions, associated with independent stopping conditions built from a canary judgment to evaluate overfitting presence. This formal basis for decision-making allows for interrupting the learning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new method to stop training neural networks when they become too good at fitting the data, which is called overfitting. This helps make predictions more accurate by stopping the learning process early. The technique looks at patterns in how well the network performs over time and uses that information to decide when to stop. |
Keywords
* Artificial intelligence * Early stopping * Neural network * Overfitting
