将Transformer用于关系型数据库，实现端到端直接学习。
Transformers Meet Relational Databases
- 设计模块化消息传递机制，贴合关系模型结构。
- 在多个数据集上表现优于现有模型，提升显著。
- 适合数据库与表格数据的深度学习研究者使用。
Transformer模型已广泛应用于可转化为序列到序列表示的机器学习领域，包括表格数据。然而，这种表示方式限制了其向更通用的关系型数据库场景扩展。本文提出一种贴近形式化关系模型的模块化神经消息传递方案，实现从数据库存储系统直接端到端学习表格Transformer。针对数据库环境中学习数据表示与加载的关键挑战进行优化，并在大量数据集上与多个相关领域的代表性模型对比。结果表明，新提出的神经架构性能显著更优。
原文摘要 · Abstract (English)
Transformer models have continuously expanded into all machine learning domains convertible to the underlying sequence-to-sequence representation, including tabular data. However, while ubiquitous, this representation restricts their extension to the more general case of relational databases. In this paper, we introduce a modular neural message-passing scheme that closely adheres to the formal relational model, enabling direct end-to-end learning of tabular Transformers from database storage systems. We address the challenges of appropriate learning data representation and loading, which are critical in the database setting, and compare our approach against a number of representative models from various related fields across a significantly wide range of datasets. Our results demonstrate a superior performance of this newly proposed class of neural architectures.
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