Papers › SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
Tabular data underpins numerous high-impact applications of machine learning from fraud detection to genomics and healthcare. Classical approaches to solving tabular problems, such as gradient boosting and random forests, are widely used by practitioners. However, recent deep learning methods have achieved a degree of performance competitive with popular techniques. We devise a hybrid deep learning approach to solving tabular data problems. Our method, SAINT, performs attention over both rows and columns, and it includes an enhanced embedding method. We also study a new contrastive self-supervised pre-training method for use when labels are scarce. SAINT consistently improves performance over previous deep learning methods, and it even outperforms gradient boosting methods, including XGBoost, CatBoost, and LightGBM, on average over a variety of benchmark tasks.
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Code
Syntology Ran 3 of 26 code samples harvested from 4 repositories linked to this paper; 23 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · violated contract.
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Code Syntology ran Syntology
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Methods
Introduced by this paper: SAINT
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