论文标题
用功能输入深度学习
Deep Learning with Functional Inputs
论文作者
论文摘要
我们提出了一种将功能数据集成到深度连接的进发神经网络中的方法。该模型是针对具有多个功能和标量协变量的标量响应定义的。该方法的副产品是一组动态功能权重，可以在优化过程中可视化。这种可视化导致了协变量与相对于常规神经网络的反应之间的关系更大的解释性。该模型显示在许多上下文中表现良好，包括对新数据的预测和真实基础功能权重的恢复；通过实际应用和仿真研究证实了这些结果。即将到来的R软件包是在流行的深度学习库（KERA）之上开发的，允许一般使用该方法。
We present a methodology for integrating functional data into deep densely connected feed-forward neural networks. The model is defined for scalar responses with multiple functional and scalar covariates. A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process. This visualization leads to greater interpretability of the relationship between the covariates and the response relative to conventional neural networks. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying functional weights; these results were confirmed through real applications and simulation studies. A forthcoming R package is developed on top of a popular deep learning library (Keras) allowing for general use of the approach.
