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.

发展了一种基于结构化状态空间模型（SSM）MambaTab 的创新方法，用于表格数据的端到端监督学习，相对于现有技术的基准，MambaTab 表现出更高的性能，在需要更少的参数和最小的预处理的同时经过了多样化基准数据集的实证验证，其高效性、可扩展性、普适性和预测性的增益表明其在广泛的实际应用上作为一种轻量级、即插即用的解决方案具有潜力。