提高隐藏的神经随机微分方程的噪声估计
L Heck1, M Gelbrecht2, M T Schaub3
1Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
Chaos (Woodbury, N.Y.)
|June 24, 2025
概括
潜在的神经随机微分方程 (SDEs) 现在可以更好地建模随机时间序列数据. 一种新的噪声规范化技术提高了它们在捕获数据扩散和动态方面的准确性.
科学领域:
- 机器学习 机器学习
- 随机过程 随机过程
- 生成型模型 生成型模型
背景情况:
- 潜在的神经随机微分方程 (SDEs) 正在出现,用于生成时间序列的建模.
- 现有的潜在神经SDEs低估了噪音,限制了准确的随机动态建模.
研究的目的:
- 调查潜伏神经SDEs中的噪音低估.
- 提出一种解决方案,以提高随机时间序列数据的准确建模.
主要方法:
- 在损失函数中引入了一个明确的额外的噪声规范化术语.
- 在表现出随机可比动态的概念系统上对改进的模型进行了评估.
主要成果:
- 拟议的噪音规范化成功解决了低估噪音水平的问题.
- 增强的潜在神经SDE准确地捕获了数据的扩散组件.
- 在建模随机双可变动力学方面表现出更好的能力.
结论:
- 简单的噪声调节技术显著提高了潜在的神经SDE性能.
- 这种方法可以更准确地生成随机时间序列的建模.
- 这种方法对于捕捉复杂的随机动态是有效的.
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