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English(EN) Generalization Can Emerge in Tabular Foundation Models From a Single Table

表格基础模型用更少的数据展现出强大的泛化能力

作者 PulseAugur 编辑部 · [2 个来源] ·

新研究表明，表格基础模型（TFMs）可以用比之前想象的少得多的数据实现泛化能力。一项研究表明，在单个真实表格上进行预训练可以在各种基准测试中产生强大的迁移学习能力，这挑战了对大型合成或真实世界数据集的必要性。另一项对TFMs的调查表明，虽然特征工程随着更先进的模型而影响减小，但提供来自相关数据集的上下文信息对于性能仍然至关重要。 AI

影响 表明先进的表格基础模型在有效训练和泛化方面可能需要更少的数据。

排序理由 两篇arXiv论文提出了关于表格基础模型的新发现。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

表格基础模型用更少的数据展现出强大的泛化能力

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两篇arXiv论文提出了关于表格基础模型的新发现。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Junwei Ma, Nour Shaheen, Alex Labach, Amine Mhedhbi, Frank Hutter, Anthony L. Caterini, Valentin Thomas ·

    表格基础模型中的泛化能力可从单个表格中涌现

    arXiv:2511.09665v1 Announce Type: cross Abstract: Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of $(x,y)$ pairs as context and predicts labels for new inputs without weight updates. We challenge the prevailing v…

  2. arXiv cs.LG TIER_1 English(EN) · Yifan WU, Pinjun Dong, Jiran Tao, Binyan Jiang ·

    表格基础模型是否还需要特征工程？

    arXiv:2609.13202v1 Announce Type: new Abstract: Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular datasets and applied via in-context learning. Their rise raises a natural questi…

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