论文标题
超越均值场：结构化的深高斯过程改善了预测性不确定性
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
论文作者
论文摘要
深层高斯流程通过级联多个高斯流程来学习概率数据表示，以进行监督学习。尽管该模型家族有望灵活的预测分布，但精确的推断并不是可行的。近似推理技术与融合速度和计算效率的后验分布非常相似。我们提出了一个新型的高斯变分家族，该家族允许在潜在过程之间保留协方差，同时通过边缘化所有全球潜在变量来实现快速收敛。在提供了如何对一般协方差进行边缘化的证明之后，我们将它们限制在我们经验上发现的那些边缘化，这对于还可以达到计算效率而言是最重要的。我们提供了新方法的有效实施，并将其应用于多个基准数据集。与其最先进的替代方案相比，它可以产生出色的结果，并在准确性和校准的不确定性估计之间取得更好的平衡。
Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predictive distributions, exact inference is not tractable. Approximate inference techniques trade off the ability to closely resemble the posterior distribution against speed of convergence and computational efficiency. We propose a novel Gaussian variational family that allows for retaining covariances between latent processes while achieving fast convergence by marginalising out all global latent variables. After providing a proof of how this marginalisation can be done for general covariances, we restrict them to the ones we empirically found to be most important in order to also achieve computational efficiency. We provide an efficient implementation of our new approach and apply it to several benchmark datasets. It yields excellent results and strikes a better balance between accuracy and calibrated uncertainty estimates than its state-of-the-art alternatives.
