English(EN) Conditioning Gaussian Processes on Almost Anything

高斯过程现可通过扩散模型进行自然语言条件化

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-20 11:23

研究人员开发了一种新颖的方法，可以将高斯过程（GPs）条件化于包括自然语言在内的广泛信息。该方法建立了高斯过程与线性扩散模型之间的等价性，允许将预测采样视为一个常微分方程（ODE）。新技术使高斯过程能够整合多样化的现实世界知识，例如非线性物理和来自大型语言模型的文本，以实现更强大的概率建模。 AI

影响通过将包括自然语言在内的多样化现实世界数据整合到高斯过程中，实现了更灵活、更强大的概率建模。

排序理由该集群包含一篇详细介绍高斯过程新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Henry Moss, Lachlan Astfalck, Thomas Cowperthwaite, Colin Doumont, Sam Willis, Philipp Hennig, Christopher Nemeth, Andrew Zammit-Mangion · 2026-05-21 04:00

Conditioning Gaussian Processes on Almost Anything

arXiv:2605.21041v1 Announce Type: new Abstract: Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalence between GPs and a class of linear diffusion models…
arXiv stat.ML TIER_1 English(EN) · Andrew Zammit-Mangion · 2026-05-20 11:23

Conditioning Gaussian Processes on Almost Anything

Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalence between GPs and a class of linear diffusion models, recasting predictive sampling as an ODE with c…