{"id":"W2797819729","doi":"10.1109/tkde.2018.2828095","title":"Fast Cosine Similarity Search in Binary Space with Angular Multi-Index Hashing","year":2018,"lang":"en","type":"preprint","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hamming distance; Nearest neighbor search; Binary code; Hash function; Hash table; Cosine similarity; Dynamic perfect hashing; Hamming space; Binary number; Computer science; Locality-sensitive hashing; Similarity (geometry); Linear search; Algorithm; Binary search algorithm; Hamming code; Search algorithm; Mathematics; Theoretical computer science; Double hashing; Data mining; Pattern recognition (psychology); Block code; Artificial intelligence; Decoding methods","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001108263,0.0007947744,0.001610065,0.001713532,0.0007046135,0.002064161,0.002116777,0.00113114,0.005474028],"category_scores_gemma":[0.007152548,0.0004747257,0.0007870132,0.004218233,0.000650296,0.004229808,0.002487395,0.001015567,0.003814782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000755384,"about_ca_system_score_gemma":0.002154344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003433408,"about_ca_topic_score_gemma":0.004381925,"domain_scores_codex":[0.9982466,0.0002519774,0.0002091091,0.0004881994,0.0006484427,0.0001557701],"domain_scores_gemma":[0.9980483,0.0006695286,0.0002215008,0.0006190545,0.0003528316,0.00008873294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001254018,0.0004043803,0.005907569,0.001047336,0.0001531281,0.0003533273,0.0005506022,0.09674953,0.04057268,0.04065436,0.02242889,0.7899241],"study_design_scores_gemma":[0.0002207949,0.0003517554,0.001685903,0.00005515456,0.00003497369,0.0006154565,0.000317254,0.9285795,0.01872558,0.0398373,0.00950141,0.00007494062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05264004,0.001354405,0.9364515,0.0003568353,0.0001781453,0.0002711712,0.00125041,0.003931237,0.003566195],"genre_scores_gemma":[0.2392637,0.0005100853,0.7527263,0.0002192913,0.0001266119,0.0003536008,0.003663352,0.0002395608,0.002897612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005474028,"threshold_uncertainty_score":0.01831245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04571143733524703,"score_gpt":0.3195922724737524,"score_spread":0.2738808351385054,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}