用Transformer架构提升表格数据深度学习表现，效果媲美树模型。
Improving Deep Tabular Learning
- 基于Transformer设计规则嵌入与分段线性量化，适配异构表格特征
- 在8个基准数据集上超越或持平主流树模型，计算开销低
- 适合需要神经网络方案且关注不确定性估计的表格任务
表格数据仍是现实世界信息的主要形式，但因其特征类型多样、缺乏自然结构及标签保持的增强手段，深度学习面临持续挑战。目前，基于决策树的集成模型仍主导排行榜。本文提出RuleNet，一种专为深度表格学习设计的Transformer架构。其包含可学习规则嵌入的解码器、用于数值特征的分段线性分位数投影，以及用于鲁棒性和不确定性估计的特征掩码集成。在8个基准数据集上的评估显示，RuleNet在多数情况下达到或超过现有树基方法性能，同时保持计算高效，为表格预测任务提供了一种实用的神经网络替代方案。
原文摘要 · Abstract (English)
Tabular data remain a dominant form of real-world information but pose persistent challenges for deep learning due to heterogeneous feature types, lack of natural structure, and limited label-preserving augmentations. As a result, ensemble models based on decision trees continue to dominate benchmark leaderboards. In this work, we introduce RuleNet, a transformer-based architecture specifically designed for deep tabular learning. RuleNet incorporates learnable rule embeddings in a decoder, a piecewise linear quantile projection for numerical features, and feature masking ensembles for robustness and uncertainty estimation. Evaluated on eight benchmark datasets, RuleNet matches or surpasses state-of-the-art tree-based methods in most cases, while remaining computationally efficient, offering a practical neural alternative for tabular prediction tasks.
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