27 citations · 90 across the 13 of their papers we have counts for
14 papers
DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers
Philip Adams, Menghao Li, Shi Zhang +6
We present DISTRIBUTEDANN, a distributed vector search service that makes it possible to search over a single 50 billion vector graph index spread across over a thousand machines t…
Cost-Effective, Low Latency Vector Search with Azure Cosmos DB
Nitish Upreti, Harsha Vardhan Simhadri, Hari Sudan Sundar +33
Vector indexing enables semantic search over diverse corpora and has become an important interface to databases for both users and AI agents. Efficient vector search requires deep…
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search
Haike Xu, Magdalen Dobson Manohar, Philip A. Bernstein +3
Indices for approximate nearest neighbor search (ANNS) are a basic component for information retrieval and widely used in database, search, recommendation and RAG systems. In these…
Results of the Big ANN: NeurIPS'23 competition
Harsha Vardhan Simhadri, Martin Aumüller, Amir Ingber +20
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate…
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Qi Chen, Xiubo Geng, Corby Rosset +28
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-s…
BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU
Karthik V., Saim Khan, Somesh Singh +2
Approximate Nearest Neighbour Search (ANNS) is a subroutine in algorithms routinely employed in information retrieval, pattern recognition, data mining, image processing, and beyon…