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Found 3 papers, 1 papers with code
CARTE: pretraining and transfer for tabular learning
The architecture - CARTE for Context Aware Representation of Table Entries - uses a graph representation of tabular (or relational) data to process tables with different columns, string embeddings of entries and columns names to model an open vocabulary, and a graph-attentional network to contextualize entries with column names and neighboring entries.
Vectorizing string entries for data processing on tables: when are larger language models better?
We study the benefits of language models in 14 analytical tasks on tables while varying the training size, as well as for a fuzzy join benchmark.
Why do tree-based models still outperform deep learning on tabular data?
While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear.
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