MambaTab: A Plug-and-Play Model for Learning Tabular Data.

Area of Science:

• Machine Learning
• Data Science
• Artificial Intelligence

Background:

• Tabular data remains crucial across many fields, despite the rise of image and text data.
• Current deep learning models require extensive preprocessing and tuning, limiting their practical use.
• Structured State-Space Models (SSMs) show promise for handling data with long-range dependencies.

Purpose of the Study:

• To introduce MambaTab, an innovative deep learning approach for tabular data analysis.
• To leverage the Mamba variant of SSMs for efficient end-to-end supervised learning on tables.
• To demonstrate MambaTab's effectiveness compared to existing state-of-the-art methods.

Main Methods:

• Developed MambaTab, a novel model based on structured state-space models (SSMs).
• Utilized the Mamba architecture for end-to-end supervised learning on tabular datasets.
• Conducted empirical validation on diverse benchmark datasets.

Main Results:

• MambaTab achieved superior performance compared to state-of-the-art baselines.
• The model demonstrated significantly fewer parameter requirements.
• Empirical validation confirmed efficiency, scalability, and generalizability.

Conclusions:

• MambaTab offers a lightweight, "plug-and-play" solution for tabular data.
• The model shows significant predictive gains and broader practical application potential.
• MambaTab advances efficient deep learning for structured data analysis.