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Mambular: A Sequential Model for Tabular Deep Learning

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arxiv 2408.06291 v2 pith:SOEOI3AE submitted 2024-08-12 cs.LG

Anton Frederik Thielmann , Manish Kumar , Christoph Weisser , Arik Reuter , Benjamin Säfken , Soheila Samiee This is my paper · ORCID
classification cs.LG
keywords tabulardatamodelsdeeplearninganalysisautoregressivechallenging
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The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features. However, recent deep learning innovations are challenging this dominance. This paper investigates the use of autoregressive state-space models for tabular data and compares their performance against established benchmark models. Additionally, we explore various adaptations of these models, including different pooling strategies, feature interaction mechanisms, and bi-directional processing techniques to understand their effectiveness for tabular data. Our findings indicate that interpreting features as a sequence and processing them and their interactions through structured state-space layers can lead to significant performance improvement. This research underscores the versatility of autoregressive models in tabular data analysis, positioning them as a promising alternative that could substantially enhance deep learning capabilities in this traditionally challenging area. The source code is available at https://github.com/basf/mamba-tabular.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TabFlex: Scaling Tabular Learning to Millions with Linear Attention

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Linear attention lets a TabPFN-style model process millions of tabular samples in seconds with near-identical accuracy on small datasets.

  2. iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data

    cs.CV 2026-08 conditional novelty 5.0 of 10

    iStructTab reports that ordering tabular features via a graph-based descriptor score before transformer fusion improves multimodal image-table classification on most of six benchmarks, with uneven gains.

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