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热启动还是冷启动? 基于梯度的超参数调中概括性的比较
Yubo Zhou1, Jun Shu2, Chengli Tan3
1School of Mathematics and Statistics, Xi'an Jiaotong University, China; SGIT AI Lab, State Grid Corporation of China, China.
概括
对于超参数调整的双级优化 (BO) 中的热启动策略可能会导致更糟糕的概括和过拟合. 这项研究揭示了原因,并提出了诸如随机扰动初始化等方法来提高性能.
科学领域:
- 机器学习 机器学习
- 优化优化 优化优化
背景情况:
- 双级优化 (BO) 越来越多地用于超参数调整.
- 常见的内层策略包括冷启动 (固定的初始化) 和热启动 (重用以前的解决方案).
- 之前的研究表明,热启动具有更好的收性,但其概括性不太了解.
研究的目的:
- 从概括的角度来看,比较BO中的冷启动和热启动策略,用于超参数调整.
- 从理论上解释两种策略之间的概括差异.
- 提出改善热启动策略通用化的方法.
主要方法:
- 理论分析,确定冷启动和热启动的概括界限.
- 在超参数调任务上,两种策略的实证比较.
- 开发和测试新的方法来增强热启动通用化.
主要成果:
- 与冷启动相比,热启动策略表现出更差的概括性能和更严重的过拟合.
- 理论界限显示热启动的概括上限更糟,这是由于与内部层次动态的更深层次的相互作用.
- 提出的方法,包括随机扰动初始化,有效地增强了热启动通用化.
结论:
- 内层初始化策略的选择显著影响了对超参数调整的双层优化中的概括性.
- 热启动的不良概括在理论上可以通过其依赖内部层次动态来解释.
- 新技术可以减轻热启动的通用化差距,使其与冷启动更具竞争力.
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