English(EN) SHEAF: Self-profiled Hardness Estimation from Answer-set Flux for Predicting Query Hardness in Graph-based ANN Search

新的SHEAF方法预测ANN搜索中的查询难度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-14 00:20

研究人员开发了一种名为SHEAF（答案集通量自剖面硬度估计）的新方法，用于预测基于图的近似最近邻（ANN）搜索中查询的难度。与之前的局部内在维度（LID）等静态度量不同，SHEAF通过观察查询的答案集在两个浅探针宽度之间如何变化来估计难度。该方法在SIFT1M等数据集上进行了评估，并在GPU和CPU上实现，与五个基线度量相比，显示出更高的预测精度，仅需两次浅探针搜索，且无需查询时真实值。 AI

影响通过实现动态查询处理，提高了大规模数据检索系统的效率。

排序理由学术论文，详细介绍了ANN搜索中查询难度估计的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Dongfang Zhao · 2026-07-14 00:20

SHEAF：基于答案集通量进行自我剖析的硬度估计，用于预测基于图的ANN搜索中的查询硬度

Graph-based approximate nearest neighbor (ANN) search is usually governed by a beam-width parameter that trades recall for throughput and is fixed for the whole workload. Yet, queries may not be equally hard: for example, on the widely used data set SIFT1M, the beam that a query …

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SHEAF：基于答案集通量进行自我剖析的硬度估计，用于预测基于图的ANN搜索中的查询硬度

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