English(EN) Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data

新框架提升TabPFN在大型表格数据集上的推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-14 04:00

研究人员开发了一个名为平衡自适应原型选择（BAPS）的新框架，以提高预训练表格基础模型（TabPFN）在大型数据集上的可扩展性。BAPS在不改变原始模型的情况下，为推理构建了压缩的、保留信息的上下文。在HIGGS和SUSY数据集上的实验表明，BAPS可以实现显著的上下文压缩（约1953倍），同时保持强大的预测性能和校准，从而使TabPFN能够应用于具有数百万行的imerick。 AI

影响无需重新训练即可在更大规模的数据集上使用强大的表格基础模型。

排序理由该集群包含一篇学术论文，详细介绍了一种提高模型推理可扩展性的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Mahboobe Jadid, Melika Rezaye Garkani, Ali Mousavi · 2026-08-14 04:00

面向大规模表格数据的可扩展TabPFN推理的平衡自适应原型选择

arXiv:2608.12989v1 Announce Type: new Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context. This paper introduces Balanced Adaptive Pr…

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面向大规模表格数据的可扩展TabPFN推理的平衡自适应原型选择

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