{"id":"W2221852422","doi":"","title":"Minimal Loss Hashing for Compact Binary Codes","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":704,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hash function; Binary code; Computer science; Binary number; Code (set theory); Algorithm; Hash table; Theoretical computer science; Similarity (geometry); Hinge loss; Artificial intelligence; Image (mathematics); Mathematics; Arithmetic; Set (abstract data type)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00141343,0.0005920189,0.000985742,0.0008925589,0.0004192349,0.001162276,0.001617064,0.001078609,0.002592138],"category_scores_gemma":[0.009746375,0.0003739061,0.0004548485,0.001141816,0.001290017,0.003484489,0.002789943,0.001615821,0.001285705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007389992,"about_ca_system_score_gemma":0.0007546433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005849408,"about_ca_topic_score_gemma":0.0005566736,"domain_scores_codex":[0.9984786,0.0005236575,0.00008040279,0.0002548005,0.0005489024,0.0001136717],"domain_scores_gemma":[0.997116,0.001263122,0.0002893958,0.0008884781,0.0003278159,0.0001151454],"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":[0.0006995638,0.0002015305,0.002703204,0.0003846099,0.00009192641,0.0002143504,0.0002778666,0.2601634,0.01586416,0.2023736,0.01230312,0.5047227],"study_design_scores_gemma":[0.00004437203,0.0001744919,0.0004445212,0.00002674716,0.00001253995,0.0001620975,0.00003106782,0.8659391,0.005674625,0.1253097,0.002150164,0.00003040775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01462661,0.0003645481,0.9833047,0.0001698143,0.00004603367,0.00004199327,0.0001594945,0.0005018457,0.0007849159],"genre_scores_gemma":[0.5730379,0.0006553312,0.4189613,0.0003149151,0.0002727156,0.0003197965,0.00141508,0.0001914936,0.004831559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002592138,"threshold_uncertainty_score":0.008671582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07442839610254194,"score_gpt":0.3188149895157965,"score_spread":0.2443865934132545,"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."}}