学习的随机热力学参数概率模型
1Physics Program, The Graduate Center, City University of New York, New York, NY 10016, USA.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
这项研究将机器学习视为热力学过程,引入信息理论指标来量化学习. 它表明学习信息积累与参数概率模型中的产量有关.
科学领域:
- 信息的热力学信息的热力学.
- 机器学习理论机器学习理论
- 信息理论 信息理论
背景情况:
- 机器学习问题可以被视为不断发展的参数概率模型 (PPM).
- 信息热力学提供了在学习过程中分析信息理论内容的工具.
研究的目的:
- 评估学习PPM的信息理论内容.
- 在机器学习过程中引入新的信息流量指标.
主要方法:
- 制定机器学习问题作为PPM的时间演变.
- 介绍记住的信息 (M-info) 和学习的信息 (L-info) 的指标.
- 分析L-info,产量和模型参数之间的关系.
主要成果:
- 定义了两个信息理论指标,M-info和L-info,以跟踪PPM学习中的信息流.
- L-info的积累与的产生有关.
- 模型参数充当热储库,将学习的信息存储为M-info.
结论:
- 机器学习过程可以使用热力学原理有效地建模.
- 拟议的指标为量化机器学习中的信息提供了一个框架.
- 通过热力学类比来理解信息流,可以更深入地了解模型学习和参数行为.
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