24 citations · 25 across the 10 of their papers we have counts for
6 papers · 1 filter
Time-Decayed Vector Search in the Rhythm of TANGO: Jointly Modeling Semantic Similarity and Temporal Freshness
Jiuqi Wei, Qiyao Luo, Quanqing Xu +2
Vector search typically measures relevance through semantic similarity under a fixed scoring function. However, in a growing range of applications, relevance may evolve over time,…
PDET-LSH: Scalable In-Memory Indexing for High-Dimensional Approximate Nearest Neighbor Search with Quality Guarantees
Jiuqi Wei, Xiaodong Lee, Botao Peng +3
Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search with theoretical guarantees. Traditional LSH-based methods mainly focus on i…
TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search
Jiuqi Wei, Zhenyu Liao, Ruoyu Han +3
Approximate Nearest Neighbor Search (ANNS) in high-dimensional Euclidean spaces is a fundamental problem with broad applications. Subspace Collision is a newly proposed ANNS framew…
The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI
Jiuqi Wei, Quanqing Xu, Chuanhui Yang
Modern AI and vector search are rapidly converging, forming a promising research frontier in intelligent information systems. On one hand, advances in AI have substantially improve…
Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search
Jiuqi Wei, Xiaodong Lee, Zhenyu Liao +2
Approximate Nearest Neighbor (ANN) search in high-dimensional Euclidean spaces is a fundamental problem with a wide range of applications. However, there is currently no ANN method…
DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search
Jiuqi Wei, Botao Peng, Xiaodong Lee +1
Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search in high-dimensional spaces due to its robust theoretical guarantee on query…