{"id":"W3081178730","doi":"10.1145/3394486.3403167","title":"Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model","year":2020,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Crowds; Computer science; Artificial intelligence; Generative model; Generative grammar; Machine learning","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":[0.003719402,0.00148046,0.002279603,0.001359774,0.001157668,0.001821553,0.004110267,0.002454133,0.003036991],"category_scores_gemma":[0.008009428,0.001185412,0.002193789,0.001528359,0.002526443,0.002833761,0.003440457,0.003833947,0.0009940933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002646275,"about_ca_system_score_gemma":0.00261832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0136067,"about_ca_topic_score_gemma":0.02600837,"domain_scores_codex":[0.9978916,0.0008116298,0.00007097155,0.0006596867,0.0003459901,0.0002200256],"domain_scores_gemma":[0.9932227,0.004577053,0.0005063732,0.0008825791,0.0005394985,0.0002716984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003198785,0.0002180845,0.003216201,0.000150557,0.0001414188,0.0001841134,0.0004418002,0.7991617,0.001704858,0.03264463,0.006259757,0.1555569],"study_design_scores_gemma":[0.00001076603,0.000007910458,0.00006537434,0.000006505712,0.000005579339,0.00001014706,0.000008861321,0.983743,0.0002488638,0.0155887,0.0002988257,0.00000531601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02039451,0.0003784048,0.9748394,0.0007585795,0.00006649449,0.0001107907,0.0003069263,0.001630386,0.001514548],"genre_scores_gemma":[0.7186196,0.0003857753,0.2683574,0.001247742,0.0002260218,0.0004642199,0.002458225,0.0004493268,0.007791738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0136067,"threshold_uncertainty_score":0.02705497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08181407603689725,"score_gpt":0.2707646443327881,"score_spread":0.1889505682958909,"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."}}