Irregular sampling occurs in many time series modeling applications where it
presents a significant challenge to standard deep learning models. This work is
motivated by the analysis of physiological time series data in electronic
health records, which are sparse, irregularly sampled, and multivariate. In
this paper, we propose a new deep learning framework for this setting that we
call Multi-Time Attention Networks. Multi-Time Attention Networks learn an
embedding of continuous-time values and use an attention mechanism to produce a
fixed-length representation of a time series containing a variable number of
observations. We investigate the performance of this framework on interpolation
and classification tasks using multiple datasets. Our results show that the
proposed approach performs as well or better than a range of baseline and
recently proposed models while offering significantly faster training times
than current state-of-the-art methods.

本篇论文针对电子健康档案中所涉及的稀疏、不规则采样、多变量的生理时序数据提出一种名为 Multi-Time Attention Networks 的多时间注意网络的深度学习框架，并使用多个数据集进行内插和分类任务的性能研究，结果表明该框架的性能优于许多基线和最近提出的模型，同时提供了比当前最先进方法更快的训练速度。