知识是打破了爱尔戈迪性的一种方式.
Yang He1, Vassiliy Lubchenko2,3,4
1Department of Chemistry, University of Houston, Houston, TX 77204 5003, U.S.A.
Neural computation
|March 3, 2025
概括
我们开发了一个热力学潜力来训练生成模型. 这种方法在模型中创建多个最小值.
科学领域:
- 统计力学 统计力学
- 机器学习 机器学习
- 计算物理 计算物理
背景情况:
- 生成模型对于复杂的数据分析至关重要.
- 在大相空间上训练生成模型在计算上具有挑战性.
- 了解发电模型的能源格局是它们性能的关键.
研究的目的:
- 发展热力学潜力,以指导生成模型培训.
- 在培训过程中调查生成模型中多重最小值的出现.
- 分析训练数据,自由能量和模型行为之间的关系.
主要方法:
- 为二进制的自由度构建一个热力学潜力.
- 分析生成模型的自由能源格局.
- 调查ergodicity破坏及其对学习和检索的影响.
主要成果:
- 热力学潜能指导训练,导致多个自由能量最小值.
- 训练组中的ergodicity破裂将其与高温阶段分开.
- 多个最小值可以限制访问代表性不足的模式,但可以提高功能.
结论:
- 训练生成模型涉及抽样一个自由能量表面与不同的束状态.
- 突破ergodicity对于模型的功能至关重要,但可能会阻碍学习.
- 采用多个生成模型,至少一个,可以减轻学习和检索问题.
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