改进隐变量神经随机微分方程的噪声估计，更准确捕捉数据中的随机动态。
Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
- 在损失函数中加入显式噪声正则项，修正噪声低估问题。
- 在双稳态随机系统上验证，模型能准确学习扩散成分。
- 适合研究随机动力系统的生成建模与高维时序数据建模者。
隐变量神经随机微分方程（Latent Neural SDEs）近年来成为从随机时间序列数据中学习生成模型的有前景方法。然而，该方法系统性低估数据固有的噪声水平，限制了其对随机动态的准确建模能力。本文深入分析了这一低估现象，并提出简单有效的解决方案：在损失函数中引入显式额外噪声正则项，使模型能准确学习数据的扩散成分。我们在一个概念性模型系统上验证了该方法，结果表明改进后的隐变量神经SDE能够更精确地建模双稳态随机动力学。
原文摘要 · Abstract (English)
Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they systematically underestimate the noise level inherent in such data, limiting their ability to capture stochastic dynamics accurately. We investigate this underestimation in detail and propose a straightforward solution: by including an explicit additional noise regularization in the loss function, we are able to learn a model that accurately captures the diffusion component of the data. We demonstrate our results on a conceptual model system that highlights the improved latent neural SDE's capability to model stochastic bistable dynamics.
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