Generative SDEs with Trainable Diffusion and Density-Based Losses
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Quick overview
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-dime…
- 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.
Full text
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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