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TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with overall performance largely depending on how well models can infer task-specific predictive relationships from the available context during inferenc
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