{"id":"W2982305100","doi":"","title":"Discriminative Quantization for Fast Similarity Search.","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Quantization (signal processing); Nearest neighbor search; Pattern recognition (psychology); Similarity (geometry); Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002054379,0.0001293035,0.000147289,0.0001075302,0.00009992712,0.0001885852,0.0001938518,0.000060771,0.00001580004],"category_scores_gemma":[0.00001039784,0.0001114271,0.00005371564,0.0001379997,0.00002497264,0.0008562549,0.0001831316,0.0000981191,0.00004357681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001671222,"about_ca_system_score_gemma":0.00001042216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004822344,"about_ca_topic_score_gemma":0.000001373917,"domain_scores_codex":[0.9990429,0.00006690437,0.0001687163,0.0004019229,0.0001465595,0.0001729591],"domain_scores_gemma":[0.9993353,0.0001447137,0.00006515337,0.0002034424,0.0001882134,0.00006314984],"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":[0.00001180905,0.00003838617,0.0004321016,0.00005903313,0.000004418176,0.000001446116,0.0002296147,0.000002899203,0.0004707611,0.0002822364,0.0002732464,0.998194],"study_design_scores_gemma":[0.001725515,0.002405703,0.02193645,0.0004796273,0.00001635991,0.00003599359,0.00006830099,0.8921936,0.05067812,0.02535118,0.004406101,0.0007030134],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03435512,0.00003098541,0.9641998,0.0003384314,0.0002485405,0.000504921,0.00001867355,0.000170456,0.0001330026],"genre_scores_gemma":[0.8478537,0.0001080676,0.1505284,0.001177397,0.0001127336,0.00001942325,0.000123562,0.00001432402,0.00006240176],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9974911,"threshold_uncertainty_score":0.4543865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03834193686331022,"score_gpt":0.3198599817939886,"score_spread":0.2815180449306784,"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."}}