arXiv：cs.LG· Maksym Tretiakov, Sarah Lucie Filipp, Vincent Fortuin, Ruth Misener, Ruby Sedgwick, James Odgers·· 5 小时前AI 评分23
面向高斯过程隐变量模型的摊销结构化随机变分推理
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
AI 导读
研究将摊销结构化随机变分推理（Amortized Structured Stochastic Variational Inference）用于高斯过程隐变量模型，使隐空间的变分后验可条件依赖于诱导点取值，突破了均值场变分近似的限制。这一更灵活的后验改善了数据流形上数据点重建的多项指标。
正文
Abstract:Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistemic uncertainty of this learned manifold. One model that achieves this is the Gaussian Process Latent Variable Model, in which a Gaussian Process (GP) mapping from the latent space provides an estimate of the uncertainty of the manifold. However, the effectiveness of this uncertainty estimation is limited by the mean-field variational approximation between the GP inducing points and the latent variables. In this work, we apply Amortized Structured Stochastic Variational Inference to allow the variational posterior for the latent space to be conditionally dependent on the value of the inducing points. We demonstrate that this more flexible variational posterior improves several metrics relating to the reconstruction of points on the data manifold.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.03647 [stat.ML] |
| (or arXiv:2610.03647v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03647 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: James Odgers [view email]
[v1]
Fri, 2 Oct 2026 17:32:05 UTC (19,021 KB)
来源：arXiv：cs.LG · arxiv.org