Nearest-neighbor radii converge almost surely and obey local-dimension moment bounds under polynomial and geometric mixing dependence.
A comprehen- sive survey and experimental comparison of graph-based approximate nearest neighbor search.arXiv preprint arXiv:2101.12631, 2021
9 Pith papers cite this work. Polarity classification is still indexing.
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QuIVer performs Vamana-style graph construction entirely inside a 2-bit Sign-Magnitude BQ space, achieving >=88% Recall@10 on contrastive-learning embeddings and 2.5-5.5x higher throughput than DiskANN/HNSW at matched recall with 4.7x less hot memory.
URNG and UG enable a single graph index for diverse interval-aware ANN queries by preserving monotonic searchability and structural heredity.
Slipstream exploits continuity in vector streams to reduce insertion costs in graph ANNS indexes via prior-insertion candidates and an adaptive controller, delivering up to 30.8x higher throughput at >=0.95 recall@10 on five datasets.
RACORN-1 adds adaptive search fallback to ACORN-1 to fix recall collapse at low selectivity in filtered vector search, achieving 9-26x speedups over HNSW with recovered recall on 1M-40M datasets.
EMA attaches Markers as compact summaries to graph edges for predicate-aware guidance in filtering ANN search, delivering 1.68x-12.25x speedups over prior general filtering methods while supporting dynamic updates.
QDET deploys a 7B-parameter model fine-tuned with three auxiliary tasks and RL that matches a 671B model's F1 on query-driven timeline summarization while delivering measurable gains in production search metrics.
ScaleGANN accelerates graph-based ANN index construction up to 9x faster and 6x cheaper than DiskANN by using divide-and-merge on distributed low-cost spot GPUs with optimized partitioning and a cost-aware scheduler.
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.
citing papers explorer
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Nearest-Neighbor Radii under Dependent Sampling
Nearest-neighbor radii converge almost surely and obey local-dimension moment bounds under polynomial and geometric mixing dependence.
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QuIVer: Rethinking ANN Graph Topology via Training-Free Binary Quantization
QuIVer performs Vamana-style graph construction entirely inside a 2-bit Sign-Magnitude BQ space, achieving >=88% Recall@10 on contrastive-learning embeddings and 2.5-5.5x higher throughput than DiskANN/HNSW at matched recall with 4.7x less hot memory.
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Efficient Graph Indexing for Interval-Aware Vector Search
URNG and UG enable a single graph index for diverse interval-aware ANN queries by preserving monotonic searchability and structural heredity.
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Slipstream: Locality-Aware Graph Index Construction for Streaming Approximate Nearest Neighbor Search
Slipstream exploits continuity in vector streams to reduce insertion costs in graph ANNS indexes via prior-insertion candidates and an adaptive controller, delivering up to 30.8x higher throughput at >=0.95 recall@10 on five datasets.
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RACORN-1: Adaptive Recall-Preserving Speedup for Low-Selectivity Filtered Vector Search
RACORN-1 adds adaptive search fallback to ACORN-1 to fix recall collapse at low selectivity in filtered vector search, achieving 9-26x speedups over HNSW with recovered recall on 1M-40M datasets.
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EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates
EMA attaches Markers as compact summaries to graph edges for predicate-aware guidance in filtering ANN search, delivering 1.68x-12.25x speedups over prior general filtering methods while supporting dynamic updates.
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Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search
QDET deploys a 7B-parameter model fine-tuned with three auxiliary tasks and RL that matches a 671B model's F1 on query-driven timeline summarization while delivering measurable gains in production search metrics.
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ScaleGANN: Accelerate Large-Scale ANN Indexing by Cost-effective Cloud GPUs
ScaleGANN accelerates graph-based ANN index construction up to 9x faster and 6x cheaper than DiskANN by using divide-and-merge on distributed low-cost spot GPUs with optimized partitioning and a cost-aware scheduler.
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The Faiss library
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.