Summary of Tabkanet: Tabular Data Modeling with Kolmogorov-arnold Network and Transformer, by Weihao Gao et al.
TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
by Weihao Gao, Zheng Gong, Zhuo Deng, Fuju Rong, Chucheng Chen, Lan Ma
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Tabular data modeling approach, TabKANet, is proposed to overcome limitations in learning from numerical content. By combining a Kolmogorov-Arnold Network (KAN) based Numerical Embedding Module with a Transformer architecture, TabKANet unifies encoding of both numerical and categorical features. The model demonstrates superior performance compared to Neural Networks (NNs) across multiple public datasets for binary classification, multi-class classification, and regression tasks, matching or surpassing the performance of Gradient Boosted Decision Tree models (GBDTs). The code is publicly available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TabKANet is a new way to analyze numbers in tables. It helps computers understand numerical information better by combining two important ideas: Kolmogorov-Arnold Networks and Transformers. This model works well for classifying data into categories, predicting values, and more. It even beats some other popular models like Neural Networks and Gradient Boosted Decision Trees on many datasets. You can find the code to use this model online. |
Keywords
» Artificial intelligence » Classification » Decision tree » Embedding » Regression » Transformer
