Milstein-driven neural stochastic differential equation model with uncertainty estimates

Xiao Zhang, Wei Wei, Zhen Zhang, Lei Zhang, Wei Li

科研成果: 期刊稿件文章同行评审

4 引用 (Scopus)

摘要

Incorporating uncertainty quantification into the modeling of deep learning-based model has become a research focus in the deep learning community. Within this group of methods, stochastic differential equation (SDE)-based models have demonstrated advantages in their ability to model uncertainty quantification. However, the use of Euler's method in these models introduces imprecise numerical solutions, which limits the accuracy of SDE systems and weakens the performance of the network. In this study, we build a more precise Milstein-driven SDE network (MDSDE-Net) to improve the network performance. In addition, we analyze the convergence of the Milstein scheme and theoretically guarantee the feasibility of MDSDE-Net. Experimental and theoretical results show that the MDSDE-Net outperforms existing models.

源语言英语
页(从-至)71-77
页数7
期刊Pattern Recognition Letters
174
DOI
出版状态已出版 - 10月 2023

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