{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001110491,0.0001822586,0.0001781051,0.0001336834,0.0001590484,0.00007550175,0.00009332766,0.0001355929,0.00002726651],"category_scores_gemma":[0.00001183451,0.0002100131,0.00005520202,0.0002159697,0.000007417319,0.0004536027,0.000001486957,0.00033606,0.000005967284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007444487,"about_ca_system_score_gemma":0.0000369756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004153344,"about_ca_topic_score_gemma":0.00005722341,"domain_scores_codex":[0.9992234,0.00001966714,0.0001811378,0.0002885907,0.00008513875,0.0002021063],"domain_scores_gemma":[0.9994441,0.0001595066,0.000009736036,0.0002481256,0.00005620463,0.00008237432],"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":[0.00001161166,0.00002685493,0.000002205917,0.0001697897,0.00008832613,0.000002518252,0.0001091046,0.8854181,0.09827728,0.00004953545,0.0000223197,0.01582234],"study_design_scores_gemma":[0.0004916388,0.00002136998,0.00004077241,0.00008041497,0.00006232372,0.00004832169,0.00005734967,0.98028,0.00806698,0.000005448804,0.01062578,0.0002195324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007102989,0.000703805,0.9903407,0.00002526418,0.001219671,0.0001456808,0.0001279238,0.0002626469,0.00007134533],"genre_scores_gemma":[0.9963732,0.0001363614,0.002973301,0.000007373388,0.0001068042,0.00003847076,0.00007939077,0.00004563926,0.0002394504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9892702,"threshold_uncertainty_score":0.8564085,"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."}}