{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001514603,0.00005106914,0.00006152571,0.00002080844,0.0001545066,0.0001213796,0.0001621543,0.00002254829,0.000007180111],"category_scores_gemma":[0.00009740388,0.00004630267,0.00001088033,0.0001047677,0.000006948135,0.0003472213,0.0003120986,0.0001208551,0.00000203723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006486346,"about_ca_system_score_gemma":0.00006038027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002141659,"about_ca_topic_score_gemma":0.00001591619,"domain_scores_codex":[0.9993944,0.00003222816,0.00005504095,0.0003306132,0.0000741293,0.0001136515],"domain_scores_gemma":[0.9996369,0.00006778167,0.00002278404,0.0001970299,0.00004083542,0.00003460175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001076731,0.00006519425,0.000674919,0.00002705074,0.00004493117,0.00000648499,0.001059922,0.08072506,0.003076845,0.04617673,0.004031223,0.8641009],"study_design_scores_gemma":[0.0001638499,0.00003025266,0.0002960835,0.00000269587,0.000003042302,0.00002106392,0.00003252186,0.9934135,0.0005671775,0.00105737,0.004346316,0.00006613089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005852409,0.00002692887,0.9911589,0.0008659923,0.00005401541,0.00003588574,0.00000162904,0.0001159265,0.001888332],"genre_scores_gemma":[0.4160538,0.00001491864,0.5682089,0.0002508802,0.0001246692,0.000005822831,0.00003837273,0.000008356431,0.01529424],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9126884,"threshold_uncertainty_score":0.1888168,"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."}}