论文标题
具有异性且高维输出的计算机模型的深度学习高斯流程
Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs
论文作者
论文摘要
提出了深度学习高斯工艺（DL-GP）作为分析（近似）计算机模型的方法，该方法产生异性且高维输出。计算机仿真模型具有许多应用领域，包括社会经济过程，农业，环境，生物学，工程和物理问题。输入的确定性转换是通过深度学习进行的，预测由传统的高斯过程计算。我们使用摩托车事故和埃博拉疫情的模拟来说明我们的方法论。最后，我们以未来研究的指示得出结论。
Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Computer simulation models have many areas of applications, including social-economic processes, agriculture, environmental, biology, engineering and physics problems. A deterministic transformation of inputs is performed by deep learning and predictions are calculated by traditional Gaussian Processes. We illustrate our methodology using a simulation of motorcycle accidents and simulations of an Ebola outbreak. Finally, we conclude with directions for future research.
