论文标题
深度西格玛点过程
Deep Sigma Point Processes
论文作者
论文摘要
我们介绍了深层sigma点过程，这是一类受到深高斯过程（DGP）组成结构的启发的参数模型。深sigma点过程（DSPPS）保留了（变化）DGP的许多吸引人特征，包括由内核基函数控制的迷你批次训练和预测不确定性。重要的是，由于DSPP承认一个简单的最大似然推理程序，因此所得的预测分布不会被任何后近近似值降解。在对单变量和多变量回归任务进行广泛的经验比较中，我们发现所得的预测分布明显优于使用其他概率方法获得的可扩展回归（包括变异DGPS）获得的校准，这些分布总像是变异的DGP，总体上大约是NAT per datapoint。
We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs) retain many of the attractive features of (variational) DGPs, including mini-batch training and predictive uncertainty that is controlled by kernel basis functions. Importantly, since DSPPs admit a simple maximum likelihood inference procedure, the resulting predictive distributions are not degraded by any posterior approximations. In an extensive empirical comparison on univariate and multivariate regression tasks we find that the resulting predictive distributions are significantly better calibrated than those obtained with other probabilistic methods for scalable regression, including variational DGPs--often by as much as a nat per datapoint.
