{"id":"W3190040206","doi":"10.24963/ijcai.2021/420","title":"Dual Active Learning for Both Model and Data Selection","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Machine learning; Discriminative model; Hyperparameter; Artificial intelligence; Model selection; Selection (genetic algorithm); Dual (grammatical number); Convergence (economics); Active learning (machine learning); Labeled data; Data mining","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":[0.006935613,0.001946477,0.002466115,0.001924626,0.0008366219,0.00254554,0.005674687,0.003084123,0.003359558],"category_scores_gemma":[0.01447984,0.001091603,0.001387504,0.001578889,0.002048525,0.004514753,0.004856244,0.004168849,0.00123818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184906,"about_ca_system_score_gemma":0.001507803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001664786,"about_ca_topic_score_gemma":0.002162088,"domain_scores_codex":[0.9960885,0.001909418,0.0001855662,0.0007578567,0.0008254693,0.0002331276],"domain_scores_gemma":[0.9930648,0.004238187,0.0004350304,0.00103654,0.0008916893,0.000333754],"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":[0.0005526054,0.0005794123,0.002807199,0.0003294166,0.0002233105,0.0002103272,0.0003404,0.5403028,0.01015875,0.07014892,0.007036208,0.3673107],"study_design_scores_gemma":[0.00001761619,0.00002571114,0.00003035319,0.000005655544,0.00000891637,0.00002124638,0.000008314206,0.9892371,0.001104628,0.008989668,0.0005448246,0.000005977394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003785548,0.0001981497,0.9948875,0.0001792318,0.00002617413,0.00004279908,0.0000264094,0.0003014899,0.0005526707],"genre_scores_gemma":[0.3949243,0.0004525003,0.5978907,0.0007622502,0.0002966045,0.0006742628,0.0005442493,0.0002972912,0.004157797],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006935613,"threshold_uncertainty_score":0.03667945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03484230968304197,"score_gpt":0.3099457876717046,"score_spread":0.2751034779886626,"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."}}