6 citations · 14 across the 10 of their papers we have counts for
7 papers · 1 filter
RaBitQ-SSD: Split Codes and Pipelined I/O for SSD-Resident Vector Search
Yuexuan Xu, Jianyang Gao, Michael Norris +5
SSD-resident approximate nearest-neighbor search is essential when vector collections exceed DRAM capacity. The challenge is to reduce SSD reads and hide I/O latency through concur…
GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search
Jifan Shi, Jianyang Gao, James Xia +2
Approximate nearest neighbor search (ANNS) on GPUs is gaining increasing popularity for modern retrieval and recommendation workloads that operate over massive high-dimensional vec…
DEG: Efficient Hybrid Vector Search Using the Dynamic Edge Navigation Graph
Ziqi Yin, Jianyang Gao, Pasquale Balsebre +2
Bimodal data, such as image-text pairs, has become increasingly prevalent in the digital era. The Hybrid Vector Query (HVQ) is an effective approach for querying such data and has…
SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search
Yutong Gou, Jianyang Gao, Yuexuan Xu +1
Approximate nearest neighbor (ANN) search in high-dimensional Euclidean space has a broad range of applications. Among existing ANN algorithms, graph-based methods have shown super…
Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search
Jianyang Gao, Yutong Gou, Yuexuan Xu +3
Approximate nearest neighbor (ANN) query in high-dimensional Euclidean space is a key operator in database systems. For this query, quantization is a popular family of methods deve…
iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search
Yuexuan Xu, Jianyang Gao, Yutong Gou +2
Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-di…
RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
Jianyang Gao, Cheng Long
Searching for approximate nearest neighbors (ANN) in the high-dimensional Euclidean space is a pivotal problem. Recently, with the help of fast SIMD-based implementations, Product…