FT-Mamba: A Novel Deep Learning Model for Efficient Tabular Regression
Yongchang Chen, Yinning Liu, Guomin Chen, Qian Zhang, Yilin Wu, Jinxin Ruan · 2024
Deep learning has advanced in image, audio, and text, but challenges such as heterogeneity and weak correlations remain in tabular data. This paper presents the FT-Mamba architecture, which merges the capabilities of FT-Transformer with Mamba’s efficiency for effective, scalable sequential data processing. It also uses random augmentation, data balancing, and self-distillation to boost performance. The work provides novel insights and tools for deep learning with tabular data, improving its practical application in real-world scenarios.