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English(EN) RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search

新的RSLM方法将ANN搜索嵌入大小减少到1-4位

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的无训练向量量化方法RSLM，旨在增强近似最近邻（ANN）搜索系统。该技术将嵌入压缩到每维低至1-4位，显著降低了内存成本和带宽需求。RSLM通过编码残差向量和校正重构向量的L2范数来实现这一点，提供了比现有方法更好的质量与大小权衡。该实现利用了快速Walsh-Hadamard变换和AVX SIMD优化来实现高性能。 AI

影响 这项研究可能显著提高大规模AI模型嵌入搜索的效率和可扩展性。

排序理由 该集群描述了一篇关于ANN搜索中向量量化新算法的最新研究论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的RSLM方法将ANN搜索嵌入大小减少到1-4位

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rastislav Lenhardt, Teodora Dobos, Thomas Vecchiato, Jiri Isa, Igor Ginzburg ·

    RSLM：用于近似最近邻搜索的无训练向量量化

    arXiv:2608.30384v1 Announce Type: new Abstract: By introducing RSLM (Rotated Scaled Lloyd-Max), a family of training-free vector quantization codecs compressing embeddings to 1--4 bits per dimension, we reduce memory cost and memory bandwidth of a typical large-scale Approximate …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Igor Ginzburg ·

    RSLM：用于近似最近邻搜索的无训练向量量化

    By introducing RSLM (Rotated Scaled Lloyd-Max), a family of training-free vector quantization codecs compressing embeddings to 1--4 bits per dimension, we reduce memory cost and memory bandwidth of a typical large-scale Approximate Nearest Neighbor (ANN) search system, while redu…

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