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Jan, 2024
深度学习模型抑制过拟合的一种基于历史的方法
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
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Hao Li, Gopi Krishnan Rajbahadur, Dayi Lin, Cor-Paul Bezemer, Zhen Ming...
TL;DR
该研究提出了一种简单但功能强大的方法,通过训练历史(即验证损失)来同时检测和预防深度学习模型的过拟合,实现了优于现有方法的过拟合检测能力和预防效果。
Abstract
In
software engineering
,
deep learning models
are increasingly deployed for critical tasks such as bug
detection
and code review. However,
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