论文标题
稀疏的高斯流程变量自动编码器
Sparse Gaussian Process Variational Autoencoders
论文作者
论文摘要
大型，多维时空数据集在现代科学和工程中无处不在。处理此类数据的有效框架是高斯过程深生成模型（GP-DGM），该模型在DGM的潜在变量上采用GP先验。在GP-DGM中执行推断的现有方法不支持基于诱导点的稀疏GP近似，这对于GP的计算效率至关重要，它们也无法处理丢失的数据 - 在许多时空数据集中自然出现，这是一种原则性的方式。我们通过稀疏的高斯过程变异自动编码器（SGP-VAE）的发展来解决这些缺点，其特征在于使用部分推理网络用于参数化稀疏的GP近似值。利用摊销变异推断的好处，SGP-VAE可以在以前未观察到的数据上推断多输出稀疏GPS，而没有其他培训。 SGP-VAE在多种实验中进行了评估，在各种实验中，它的表现都超过了包括多输出GP和结构化VAE在内的替代方法。
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -- a natural occurrence in many spatio-temporal datasets -- in a principled manner. We address these shortcomings with the development of the sparse Gaussian process variational autoencoder (SGP-VAE), characterised by the use of partial inference networks for parameterising sparse GP approximations. Leveraging the benefits of amortised variational inference, the SGP-VAE enables inference in multi-output sparse GPs on previously unobserved data with no additional training. The SGP-VAE is evaluated in a variety of experiments where it outperforms alternative approaches including multi-output GPs and structured VAEs.
