English(EN) AiSAQ-in-Filtering for DRAM-free Filtered Information Retrieval

新方法整合标签和向量以减少搜索内存使用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-13 02:50

研究人员开发了一种名为 AiSAQ-in-Filtering 的新方法，以提高同时使用向量数据和标签信息的搜索应用程序的效率。该方法采用了一种新颖的量化技术 LiPQ（Label-inclusive Product Quantization），将非结构化数据和结构化标签整合到单一表示中。与现有方法相比，这种整合将 DRAM 占用空间显著减少了 78 倍，并将单标签搜索的召回率提高了 55%，同时还减少了磁盘 I/O。 AI

排序理由该集群包含一篇详细介绍信息检索新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍信息检索新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jun Deguchi · 2026-10-13 02:50

AiSAQ-in-Filtering 用于无DRAM的过滤信息检索

Filtering search is one of the crucial applications of approximate nearest neighbor search (ANNS). In filtering search, label information is used in conjunction with unstructured vector data. ANNS provides efficient similarity search in large-scale datasets, however, the memory f…

本文如何被排名

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AiSAQ-in-Filtering 用于无DRAM的过滤信息检索

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