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New Method Enhances Transfer Learning for Tabular Foundation Models
Tabular Foundation Models face challenges in transfer learning due to context-size limits and sensitivity to distribution shifts. The paper proposes a data distillation approach to overcome these obstacles and reduce negative transfer.
Improving transfer learning for TFMs can expand their applicability across diverse tasks with varying data distributions. Addressing context constraints and distribution shifts is key to more reliable and effective model reuse.
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Score 77
Source Type arxiv
Reposts 0
Topic Quality 59
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