MambaTab: A Simple Yet Effective Approach for Handling Tabular Data
Md Atik Ahamed Qiang Cheng
Tabular data remains ubiquitous across domains despite growing use of images
and texts for machine learning. While deep learning models like convolutional
neural networks and transformers achieve strong performance on tabular data,
they require extensive data preprocessing, tuning, and resources, limiting
accessibility and scalability. This work develops an innovative approach based
on a structured state-space model (SSM), MambaTab, for tabular data. SSMs have
strong capabilities for efficiently extracting effective representations from
data with long-range dependencies. MambaTab leverages Mamba, an emerging SSM
variant, for end-to-end supervised learning on tables. Compared to
state-of-the-art baselines, MambaTab delivers superior performance while
requiring significantly fewer parameters and minimal preprocessing, as
empirically validated on diverse benchmark datasets. MambaTab's efficiency,
scalability, generalizability, and predictive gains signify it as a
lightweight, "out-of-the-box" solution for diverse tabular data with promise
for enabling wider practical applications.