BBC improves large-k ANN efficiency via bucketed candidate buffers and optimized re-ranking, delivering up to 3.8x speedup at recall@k=0.95.
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GRAB-ANNS is a new GPU graph index that achieves up to 240x higher hybrid search throughput via bucket layouts and hybrid intra/inter-bucket edges.
RNSG approximates the range-aware relative neighborhood graph (RRNG) to enable high-performance range-filtered ANN queries with one compact index instead of many.
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
citing papers explorer
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BBC: Improving Large-k Approximate Nearest Neighbor Search with a Bucket-based Result Collector
BBC improves large-k ANN efficiency via bucketed candidate buffers and optimized re-ranking, delivering up to 3.8x speedup at recall@k=0.95.
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GRAB-ANNS: High-Throughput Indexing and Hybrid Search via GPU-Native Bucketing
GRAB-ANNS is a new GPU graph index that achieves up to 240x higher hybrid search throughput via bucket layouts and hybrid intra/inter-bucket edges.
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RNSG: A Range-Aware Graph Index for Efficient Range-Filtered Approximate Nearest Neighbor Search
RNSG approximates the range-aware relative neighborhood graph (RRNG) to enable high-performance range-filtered ANN queries with one compact index instead of many.
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MINT: Multi-Vector Search Index Tuning
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.