Feb. 13, 2024, 5:42 a.m. | Junwei Ma Valentin Thomas Guangwei Yu Anthony Caterini

cs.LG updates on arXiv.org arxiv.org

Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to dominate. TabPFN, a transformer model tailored for tabular data, mirrors recent foundation models in its exceptional in-context learning capability, being competitive with XGBoost's performance without the need for task-specific training or hyperparameter tuning. Despite its promise, TabPFN's applicability is hindered by its data size constraint, limiting its use in real-world scenarios. To address this, we …

and natural language processing capability computer computer vision context cs.lg data distillation foundation in-context learning language language processing natural natural language natural language processing performance processing s performance tabular tabular data tasks task-specific training training transformer transformer model tree vision xgboost

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