2 papers
cs.LG2026
Benchmarking Attention for Tabular Foundation Models
Maximilian Schambach, Clemens Biehl, Sam Thelin
Tabular in-context learners such as TabPFN, Mitra, or ConTextTab rely on alternating row and column attention over 2D sequences of latent embeddings. These attention patterns diffe…
cs.LG2025
SALT: Sales Autocompletion Linked Business Tables Dataset
Tassilo Klein, Clemens Biehl, Margarida Costa +3
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image…