{"url":"/method/saint","slug":"saint","name":"SAINT","full_name":"SAINT","full_name_withheld":false,"description_markdown":"**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: \r\n\r\n- $L$ layers with 2 attention blocks each, one self-attention block, and a novel intersample attention blocks that computes attention across samples are used.\r\n- For pre-training, this involves minimizing the contrastive and denoising losses between a given data point and its views generated by [CutMix](https://paperswithcode.com/method/cutmix) and [mixup](https://paperswithcode.com/method/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](https://paperswithcode.com/method/feedforward-network) to obtain the final prediction.","description_state":"present","introduced_year":null,"introduced_by":{"title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","paper":"/paper/saint-improved-neural-networks-for-tabular","first_author":"Gowthami Somepalli","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/saint-improved-neural-networks-for-tabular"},"source":{"url":"https://arxiv.org/abs/2106.01342v1","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Deep Tabular Learning","url":"/methods/category/deep-tabular-learning","pwc_aliases":[]}],"n_papers_tagged":9,"archive_num_papers":9,"papers_newest_first":[{"paper":"/paper/saint-attention-based-modeling-of-sub-action","title":"SAINT: Attention-Based Modeling of Sub-Action Dependencies in Multi-Action Policies","date":"2025-05-17","arxiv_id":"2505.12109","n_code_links":1,"syntology":null},{"paper":"/paper/similarity-aware-token-pruning-your-vlm-but","title":"Similarity-Aware Token Pruning: Your VLM but Faster","date":"2025-03-14","arxiv_id":"2503.11549","n_code_links":1,"syntology":null},{"paper":null,"title":"ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping","date":"2025-02-04","arxiv_id":"2502.02072","n_code_links":0,"syntology":null},{"paper":"/paper/computational-analysis-of-yaredawi-yezema","title":"Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants","date":"2024-12-25","arxiv_id":"2412.18788","n_code_links":1,"syntology":null},{"paper":null,"title":"A Survey on Deep Tabular Learning","date":"2024-10-15","arxiv_id":"2410.12034","n_code_links":0,"syntology":null},{"paper":null,"title":"Semi-Quantitative Analysis and Seroepidemiological Evidence of Past Dengue Virus Infection among HIV-infected patients in Onitsha, Anambra State, Nigeria","date":"2024-03-23","arxiv_id":"2403.15685","n_code_links":0,"syntology":null},{"paper":null,"title":"Tabular Machine Learning Methods for Predicting Gas Turbine Emissions","date":"2023-07-17","arxiv_id":"2307.08386","n_code_links":0,"syntology":null},{"paper":null,"title":"Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media","date":"2022-05-24","arxiv_id":"2205.12382","n_code_links":0,"syntology":null},{"paper":"/paper/saint-improved-neural-networks-for-tabular","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","date":"2021-06-02","arxiv_id":"2106.01342","n_code_links":7,"syntology":{"ran":3,"of":26,"unverified":23,"pointer_only":13}}],"papers_shown":9,"tasks":[{"task":"/task/deep-learning","name":"Deep Learning","papers":1},{"task":"/task/denoising","name":"Denoising","papers":1},{"task":"/task/fraud-detection","name":"Fraud Detection","papers":1},{"task":"/task/information-retrieval","name":"Information Retrieval","papers":1},{"task":null,"name":"Insurance Prediction","papers":1},{"task":"/task/missing-values","name":"Missing Values","papers":1},{"task":"/task/music-information-retrieval","name":"Music Information Retrieval","papers":1},{"task":"/task/survey","name":"Survey","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1},{"task":"/task/feature-selection","name":"feature selection","papers":1}],"tasks_shown":10,"n_tasks":10,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":1},{"year":"2023","papers":1},{"year":"2024","papers":3},{"year":"2025","papers":3}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/saint"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}