English(EN) How Many Samples Are Enough for Learning Across Domains?

新研究概述了跨领域学习的样本要求

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

一篇新发表在arXiv上的研究论文探讨了跨不同领域有效学习所需的样本数量。该研究为每个域的样本充分性设定了标准，揭示了训练域数量与每个域所需样本数量之间存在反向线性标度律。这项工作还展示了域内学习与域外泛化之间的紧密联系，为数据集构建和评估提供了理论指导。 AI

影响为评估数据集充分性和构建数据集以提高泛化能力提供了理论指导。

排序理由该条目是一篇发表在arXiv上的研究论文，讨论了机器学习的理论方面。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目是一篇发表在arXiv上的研究论文，讨论了机器学习的理论方面。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.LG TIER_1 English(EN) · Hong Zheng · 2026-10-02 04:00

跨领域学习需要多少样本才够？

arXiv:2609.39336v1 Announce Type: new Abstract: Understanding the fundamental mechanisms of learning is essential for designing systems with strong generalization. Recent studies have shown that increasing the number of training domains, or enlarging the distribution shift among …