{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007653112,0.0001769438,0.0001893683,0.00002707525,0.0002604229,0.0002992569,0.0005890918,0.0000757103,0.0001496687],"category_scores_gemma":[0.00003107228,0.0001574662,0.00005372833,0.0002333934,0.00003549976,0.0006615689,0.0003673251,0.000252015,0.0001164507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002437718,"about_ca_system_score_gemma":0.00009232017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005607371,"about_ca_topic_score_gemma":0.00001157503,"domain_scores_codex":[0.9986579,0.00006328702,0.0002134334,0.0005466761,0.0002421939,0.0002764403],"domain_scores_gemma":[0.9994477,0.00002920412,0.00006880287,0.0001900754,0.0000800034,0.0001842244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001829303,0.0001032652,0.0004171737,0.00003080063,0.00008395398,0.0000485681,0.02220181,0.1985224,0.5514852,0.002280794,0.001798618,0.2230091],"study_design_scores_gemma":[0.0006268891,0.00002509691,0.0000144426,0.000007303282,0.000006893462,0.000001077678,0.00004441039,0.9594203,0.03905388,0.0004641817,0.0001220885,0.0002134181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02077728,0.0001420633,0.9747506,0.002433695,0.0001034006,0.00007447007,0.000001059145,0.0003416678,0.001375804],"genre_scores_gemma":[0.465182,0.000004039533,0.527888,0.006379528,0.0001490335,0.000004908502,0.000005419433,0.00001101713,0.0003760655],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7608979,"threshold_uncertainty_score":0.6421286,"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."}}