Modern tabular deep learning methods, including numerical embeddings, retrieval heads, and TabM ensembles, gain most of their advantage on high-data-uncertainty samples, and a triplet-trained embedding built from this insight outperforms standard embeddings.
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Unveiling the Role of Data Uncertainty in Tabular Deep Learning
Modern tabular deep learning methods, including numerical embeddings, retrieval heads, and TabM ensembles, gain most of their advantage on high-data-uncertainty samples, and a triplet-trained embedding built from this insight outperforms standard embeddings.