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KNNG-CS slashes selection time by up to 41.2 times while preserving model accuracy, revolutionizing coreset selection for large datasets.
Privacy-preserving RFANNS can now be performed on encrypted vector databases without compromising the efficiency of search queries.
FROG achieves up to 37.7x faster RFANNS query throughput on GPUs, revolutionizing how vector databases handle range-filtering tasks.
RRANN can achieve up to 12.5x faster query times while maintaining accuracy, revolutionizing how we handle complex range queries in ANN search.
