论文标题
稀疏高斯过程中诱导点的概率选择
Probabilistic selection of inducing points in sparse Gaussian processes
论文作者
论文摘要
稀疏的高斯工艺及其各种扩展是通过诱导点来实现的，这些点同时瓶颈预测能力并充当模型复杂性的主要贡献者。但是，诱导点的数量通常与不确定性无关，这使我们无法应用贝叶斯推理的设备来识别适当的权衡。在这项工作中，我们将点过程提前放在诱导点上，并通过随机变化推断近似相关的后部。通过让先验鼓励中等数量的诱导点，我们使模型能够了解使用哪个点和数量。我们从实验上表明，随着该点的信息降低，模型首选的诱导点更少，并进一步证明了如何在深层高斯过程和潜在可变建模中使用该方法。
Sparse Gaussian processes and various extensions thereof are enabled through inducing points, that simultaneously bottleneck the predictive capacity and act as the main contributor towards model complexity. However, the number of inducing points is generally not associated with uncertainty which prevents us from applying the apparatus of Bayesian reasoning for identifying an appropriate trade-off. In this work we place a point process prior on the inducing points and approximate the associated posterior through stochastic variational inference. By letting the prior encourage a moderate number of inducing points, we enable the model to learn which and how many points to utilise. We experimentally show that fewer inducing points are preferred by the model as the points become less informative, and further demonstrate how the method can be employed in deep Gaussian processes and latent variable modelling.
