Generative SDEs with Trainable Diffusion and Density-Based Losses

Computer Science > Machine Learning

arXiv:2610.41243v1 (cs) [Submitted on 30 Oct 2026]

Title:Generative SDEs with Trainable Diffusion and Density-Based Losses

Authors:Angxiu Ni View a PDF of the paper titled Generative SDEs with Trainable Diffusion and Density-Based Losses, by Angxiu Ni View PDF HTML (experimental)

Abstract:We develop a divergence-kernel method for jointly optimizing neural SDE drift and diffusion fields and the initial Gaussian distribution, using losses that depend directly on the terminal density. In two- and eight-dimensional Gaussian-mixture experiments, we learn time-independent fields and obtain far fewer off-mode samples than score-based generators with similar parameter counts. The drift attracts samples toward the modes, while the diffusion helps disperse excess probability between them.

Comments:	17 pages, 5 figures
Subjects:	Machine Learning (cs.LG); Dynamical Systems (math.DS)
Cite as:	arXiv:2610.41243 [cs.LG]
	(or arXiv:2610.41243v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.41243 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Angxiu Ni [view email]
[v1] Fri, 30 Oct 2026 04:35:59 UTC (787 KB)
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