English(EN) Feature Normalization under Finite Training Budgets

新研究分析了特征归一化对有限训练预算的影响

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-14 04:00

本文探讨了特征标准化对资源有限的机器学习模型训练的影响。文章分析了标准化稀有特征如何影响学习过程和可达风险，尤其是在使用随机梯度下降时。研究推导了在各种条件下（包括流行度消失和有限观测预算）标准化和未缩放训练的优化风险，并考察了噪声和预算如何影响标准化与不缩放之间的选择。 AI

影响为在资源受限情况下优化机器学习训练提供了理论见解。

排序理由该条目是发表在arXiv上的关于机器学习技术的学术论文。[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: 3 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目是发表在arXiv上的关于机器学习技术的学术论文。[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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

arXiv stat.ML TIER_1 English(EN) · Johan Larsson · 2026-10-14 04:00

有限训练预算下的特征归一化

arXiv:2610.16845v1 Announce Type: new Abstract: Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learning. We characterize the lowest rare-subgroup risks attainable by standardized and…