Gaussian Process Latent Variable Model 引入摊销结构化变分推断
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
用摊销结构化变分推断改进 GP 隐变量模型，让流形的不确定性估计更准，做降维和 GP 推理的可以看看这篇论文。
这篇 arXiv 论文针对 Gaussian Process Latent Variable Model（GPLVM）的推理瓶颈：GP 诱导点与隐变量之间的 mean-field 变分近似限制了流形不确定性估计的效果。作者采用 Amortized Structured Stochastic Variational Inference，使隐空间的变分后验可以有条件地依赖诱导点的取值。实验显示这种更灵活的变分后验在数据流形重建相关的多个指标上均有提升。
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
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.