{"id":"W3188412909","doi":"10.24963/ijcai.2021/449","title":"Multi-level Generative Models for Partial Label Learning with Non-random Label Noise","year":2021,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Institute for Advanced Research","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Generator (circuit theory); Noise (video); Pattern recognition (psychology); Feature (linguistics); Multi-label classification; Machine learning; Noise reduction; Image (mathematics); Power (physics)","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":[0.002622436,0.001179752,0.001255408,0.0006660904,0.0005032276,0.001190158,0.002713395,0.00188235,0.002225656],"category_scores_gemma":[0.006494223,0.0006187724,0.001394191,0.0006690587,0.002310368,0.002251457,0.002927186,0.003215324,0.0007840198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119497,"about_ca_system_score_gemma":0.0007418154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001956854,"about_ca_topic_score_gemma":0.003582351,"domain_scores_codex":[0.9985402,0.0006083096,0.00004354081,0.0003918367,0.0002927593,0.0001234083],"domain_scores_gemma":[0.9956985,0.002777897,0.0003242391,0.0007353075,0.0003208738,0.0001432209],"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":[0.0001896893,0.00009069757,0.002432848,0.0001138114,0.00008925695,0.000159373,0.0002175616,0.8638749,0.004137379,0.04321528,0.002999209,0.08247991],"study_design_scores_gemma":[0.00000614482,0.00001755401,0.00009266513,0.000006052335,0.00000658327,0.00002484171,0.000006364097,0.982671,0.0007073439,0.01601719,0.0004385738,0.000005771369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01030065,0.0002172185,0.987931,0.000243706,0.00002318571,0.00003247759,0.00009614234,0.0004773257,0.0006783493],"genre_scores_gemma":[0.7108477,0.0004291568,0.2778995,0.0008598021,0.0001392072,0.0003425539,0.001363898,0.0003790887,0.007739054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002713395,"threshold_uncertainty_score":0.01386893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08759312322648306,"score_gpt":0.2919987879936194,"score_spread":0.2044056647671363,"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."}}