{"id":"W3128735821","doi":"10.1109/tkde.2021.3054671","title":"Transfer Learning for Dynamic Feature Extraction Using Variational Bayesian Inference","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Inference; Bayesian inference; Weighting; Feature (linguistics); Machine learning; Transfer of learning; Artificial intelligence; Data mining; Bayesian probability; Domain (mathematical analysis); Mathematics","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":[0.002615644,0.001166678,0.001363697,0.001288548,0.0005192772,0.001114937,0.002161351,0.001471478,0.002701658],"category_scores_gemma":[0.007393206,0.001013521,0.001663777,0.00126406,0.001188444,0.001885062,0.002178943,0.002693261,0.0005656411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363885,"about_ca_system_score_gemma":0.001551422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008378936,"about_ca_topic_score_gemma":0.006406134,"domain_scores_codex":[0.9992092,0.0002734325,0.00004800809,0.0001904578,0.0001977527,0.00008120042],"domain_scores_gemma":[0.9975757,0.001902999,0.0001332929,0.0001094065,0.0002324761,0.00004614254],"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":[0.0000449515,0.00004735298,0.0004642849,0.00007921138,0.00006469584,0.00005700704,0.00007015337,0.8954777,0.001493149,0.02091551,0.0008803271,0.08040562],"study_design_scores_gemma":[0.000001973974,0.000004290163,0.00002782584,0.000002219649,0.000002066897,0.000003697505,0.000001599107,0.9947082,0.0001448475,0.00498261,0.0001178792,0.000002773707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001512161,0.00007069647,0.9980029,0.00004771939,0.000006530469,0.00001627813,0.00002310514,0.000114813,0.0002057435],"genre_scores_gemma":[0.5020055,0.0006293199,0.4913394,0.0002465135,0.0001196159,0.0006138463,0.0006957216,0.0003089919,0.004041085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008378936,"threshold_uncertainty_score":0.01666033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656466914588841,"score_gpt":0.2718116489394927,"score_spread":0.2552469797936043,"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."}}