Methods › General › Deep Tabular Learning › AutoInt
AutoInt
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
AutoInt is a deep tabular learning method that models high-order feature interactions of input features. AutoInt can be applied to both numerical and categorical input features. Specifically, both the numerical and categorical features are mapped into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled.
Papers archive 2025-07-28
2 shown of 2, 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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Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction 26 Jul 2021 · 0 repositories · arXiv:2107.12024
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AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks 29 Oct 2018 · 19 repositories · arXiv:1810.11921Syntology ran 0 of 3 samples · 3 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
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
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