{"id":"W2948606716","doi":"10.1016/j.neucom.2021.11.027","title":"Bayesian active learning with abstention feedbacks","year":2021,"lang":"en","type":"preprint","venue":"Neurocomputing","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation; Viet Nam National University Ho Chi Minh City; Canada Research Chairs; Dalhousie University; University of Delaware","keywords":"Computer science; Bayesian probability; Function (biology); Value (mathematics); Greedy algorithm; Artificial intelligence; Constant (computer programming); Mathematical optimization; Machine learning; Algorithm; Mathematics","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":[0.00734526,0.001419642,0.002315999,0.001375584,0.0009916528,0.002764853,0.004382251,0.004520947,0.008392032],"category_scores_gemma":[0.03375097,0.001380626,0.0009642898,0.0013408,0.002843575,0.006672514,0.004306524,0.004864162,0.001848455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009955467,"about_ca_system_score_gemma":0.001328751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001799572,"about_ca_topic_score_gemma":0.002203064,"domain_scores_codex":[0.9960585,0.001949247,0.0002019094,0.000630081,0.0009533649,0.0002070029],"domain_scores_gemma":[0.9800331,0.01532714,0.0005836123,0.001735166,0.001770659,0.0005504206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008971159,0.0004238912,0.0008756216,0.0003977264,0.0001711,0.0001289616,0.00026715,0.3501181,0.003501411,0.4138498,0.008978089,0.220391],"study_design_scores_gemma":[0.00004364329,0.00004050554,0.00004290618,0.00001669185,0.0000149156,0.00002159776,0.00000618486,0.8755792,0.0006777281,0.122765,0.0007780116,0.00001355298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006700044,0.0003736933,0.9891552,0.0006064249,0.0001431054,0.00004443797,0.0000839228,0.0002885968,0.00260455],"genre_scores_gemma":[0.6508704,0.0006823726,0.3241397,0.0008574978,0.0008881129,0.000498335,0.0005218145,0.0003877263,0.02115411],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008392032,"threshold_uncertainty_score":0.0388459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007621210456470815,"score_gpt":0.2315422988742124,"score_spread":0.2239210884177416,"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."}}