Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data
cs.LG
Submitted: 2026-08-28
Updated: 2026-08-28
Comments: Accepted for publication at Neural Networks (Elsevier)
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics across disparate domains.
Terminology
Abstract
Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics across disparate domains. Existing methods assume shared features across data tables to enable knowledge transfer between domains, which is unrealistic in practice. The CATTLE source code is available at https://tinyurl.com/pr5s8ywn.
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