LimiX-2M outperforms larger TabPFN-v2 and TabICL models on tabular benchmarks by expanding scalars into RBF features and using a reordered S->N->F attention block.
arXiv preprint arXiv:2206.08564 , year =
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LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
LimiX-2M outperforms larger TabPFN-v2 and TabICL models on tabular benchmarks by expanding scalars into RBF features and using a reordered S->N->F attention block.