Improving Deep Tabular Learning

• URL: http://arxiv.org/abs/2509.16354v1
• Date: Fri, 19 Sep 2025 18:51:14 GMT
• Title: Improving Deep Tabular Learning
• Authors: Sivan Sarafian, Yehudit Aperstein,
• Abstract summary: Tabular data remains a dominant form of real-world information but poses persistent challenges for deep learning.<n>In this work, we introduce RuleNet, a transformer-based architecture specifically designed for deep tabular learning.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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