相关实验视频
Updated: May 23, 2025

13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
8.9K
了解延迟随机梯度下降的概括性
概括
本研究探讨了用于大规模机器学习的异步延迟随机梯度下降 (SGD). 我们发现异步延迟可以改善概括错误,这与之前的悲观边界相反.
科学领域:
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 异步随机梯度下降 (SGD) 对大规模模型训练至关重要.
- 异步延迟SGD的概括性表现未得到充分探索,现有的边界是悲观的.
研究的目的:
- 为了获得SGD与异步延迟的更清晰的泛化误差极限.
- 调查异步延迟与泛化性能之间的相关性.
主要方法:
- 使用生成函数分析来确定延迟梯度算法的平均稳定性.
- 根据算法稳定性推导概括错误的上限.
主要成果:
- 建立了 $\widetilde{\mathcal {O}}(\frac{T-\tau }{n\tau }) $ 的泛化误差界限,用于二次凸面问题和 $\widetilde{\mathcal {O}}(\frac{1}{n}) $用于强烈凸面问题.
- 证明异步延迟可以减少延迟SGD的概括错误.
结论:
- 异步延迟对延迟SGD的普遍性产生了积极的影响.
- 理论发现通过实验结果得到验证,并扩展到随机延迟设置.
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