Decoders built on Gaussian processes (GPs) are enticing due to the marginalisation over the non-linear function space. Such models (also known as GP-LVMs) are often expensive and notoriously difficult to train in practice, but can be scaled using variational inference and inducing points. In this paper, we revisit active set approximations. We develop a new stochastic estimate of the log-marginal likelihood based on recently discovered links to cross-validation, and propose a computationally efficient approximation thereof. We demonstrate that the resulting stochastic active sets (SAS) approximation significantly improves the robustness of GP decoder training while reducing computational cost. The SAS-GP obtains more structure in the latent space, scales to many datapoints and learns better representations than variational autoencoders, which is rarely the case for GP decoders.

翻译：在高斯进程(GPs)上建起的Decoders由于在非线性功能空间的边际化而正在诱人,这些模型(又称GP-LVMs)在实际操作中往往费用昂贵,而且难于培训,但使用变异推论和诱导点可以扩大规模。在本文中,我们重新审视了主动设定的近似值。我们根据最近发现的交叉校准链接对日志边际可能性进行新的随机估计,并提出一个计算效率近似值的建议。我们证明,由此形成的随机活性数据集(SAS)近似极大地提高了GP decoder培训的稳健性,同时降低了计算成本。SAS-GP在潜在空间中获得了更多的结构,对许多数据点的尺度,并学到了比变异性自动电解码器更好的描述,而GPP decoders的情况很少如此。