There is a recent growing interest in applying Deep Learning techniques to tabular data, in order to replicate the success of other Artificial Intelligence areas in this structured domain. Specifically interesting is the case in which tabular data have a time dependence, such as, for instance financial transactions. However, the heterogeneity of the tabular values, in which categorical elements are mixed with numerical items, makes this adaptation difficult. In this paper we propose a Transformer architecture to represent heterogeneous time-dependent tabular data, in which numerical features are represented using a set of frequency functions and the whole network is uniformly trained with a unique loss function.

翻译：近年来，学界日益关注将深度学习技术应用于表格数据，以期在这一结构化领域复制人工智能其他领域的成功。特别值得关注的是表格数据具有时间依赖性（如金融交易）的场景。然而，表格数据中分类要素与数值项混杂存在异构性，使得这种适应过程面临困难。本文提出一种Transformer架构用于表征时间依赖的异构表格数据，其中数值特征通过一组频率函数进行表示，整个网络采用统一损失函数进行端到端训练。