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University of Toronto
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Self-supervised pre-training on just one real table can yield surprisingly strong generalization in Tabular Foundation Models, challenging conventional wisdom about dataset size.
Single-image assessments can effectively categorize building damage types, enabling timely and targeted disaster response efforts.
