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Ant Group, Hangzhou, China
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TabEmbed leapfrogs existing text embedding models to achieve SOTA performance on tabular data by reformulating tasks as semantic matching problems and using contrastive learning.
Ditch the manual feature engineering: KMLP's hybrid KAN-gMLP architecture automatically learns complex feature transformations and interactions, outperforming GBDTs on web-scale tabular data.
