Methods › General › Deep Tabular Learning › SAINT
SAINT
Introduced by Gowthami Somepalli et al. in SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SAINT is a hybrid deep learning approach to solving tabular data problems. SAINT performs attention over both rows and columns, and it includes an enhanced embedding method. The architecture, pre-training and training pipeline are as follows:
- L layers with 2 attention blocks each, one self-attention block, and a novel intersample attention blocks that computes attention across samples are used.
- For pre-training, this involves minimizing the contrastive and denoising losses between a given data point and its views generated by CutMix and mixup. During finetuning/regular training, data passes through an embedding layer and then the SAINT model. Lastly, the contextual embeddings from SAINT are used to pass only the embedding corresponding to the CLS token through an MLP to obtain the final prediction.
Papers archive 2025-07-28
9 shown of 9, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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SAINT: Attention-Based Modeling of Sub-Action Dependencies in Multi-Action Policies 17 May 2025 · 1 repository · arXiv:2505.12109
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Similarity-Aware Token Pruning: Your VLM but Faster 14 Mar 2025 · 1 repository · arXiv:2503.11549
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ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping 4 Feb 2025 · 0 repositories · arXiv:2502.02072
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Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants 25 Dec 2024 · 1 repository · arXiv:2412.18788
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A Survey on Deep Tabular Learning 15 Oct 2024 · 0 repositories · arXiv:2410.12034
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Semi-Quantitative Analysis and Seroepidemiological Evidence of Past Dengue Virus Infection among HIV-infected patients in Onitsha, Anambra State, Nigeria 23 Mar 2024 · 0 repositories · arXiv:2403.15685
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Tabular Machine Learning Methods for Predicting Gas Turbine Emissions 17 Jul 2023 · 0 repositories · arXiv:2307.08386
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Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media 24 May 2022 · 0 repositories · arXiv:2205.12382
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SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training 2 Jun 2021 · 7 repositories · arXiv:2106.01342Syntology ran 3 of 26 samples · 23 unverified · 13 pointer-only (licence)
Tasks archive 2025-07-28
10 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Deep Learning | 1 |
| Denoising | 1 |
| Fraud Detection | 1 |
| Information Retrieval | 1 |
| Insurance Prediction | 1 |
| Missing Values | 1 |
| Music Information Retrieval | 1 |
| Survey | 1 |
| Transfer Learning | 1 |
| feature selection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections