本文探讨了生成模型的数学基础，将最优传输与薛定谔桥和流匹配等技术联系起来。旨在为该领域的研究者提供这些原理的概览。 AI
排序理由 该条目是arXiv预印本，详细介绍了生成模型的数学原理。[lever_c_demoted from research: ic=1 ai=1.0]
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
论文详细介绍生成模型技术的数学原理
报道来源 [3]
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Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{\"o}dinger bridge
arXiv:2606.30053v1 Announce Type: new Abstract: These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between optimal transport and standard techniques such as Schr{\"o}dinger bridge and flow ma…
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Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences
arXiv:2410.01244v2 Announce Type: replace Abstract: We introduce a novel Wasserstein-1 ($W_1$) path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertainty Propagation (WUP) theorem that bounds the $W_1$ distance between terminal distrib…
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Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{ö}dinger bridge
These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between optimal transport and standard techniques such as Schr{ö}dinger bridge and flow matching.