{"id":"W3188523428","doi":"10.24963/ijcai.2021/354","title":"Asynchronous Active Learning with Distributed Label Querying","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Asynchronous communication; Crowdsourcing; Server; Active learning (machine learning); Synchronization (alternating current); Latency (audio); Machine learning; Artificial intelligence; Distributed computing; Computer network; World Wide Web","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":[0.00459563,0.001436759,0.00194665,0.0009333107,0.001395644,0.003105224,0.006267992,0.002467854,0.003113387],"category_scores_gemma":[0.01041272,0.0007822982,0.0008574724,0.001507787,0.00183988,0.00546817,0.004798215,0.003060711,0.001051477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120928,"about_ca_system_score_gemma":0.002010167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002216973,"about_ca_topic_score_gemma":0.003042525,"domain_scores_codex":[0.9964976,0.001153689,0.0001595381,0.00112661,0.000778458,0.0002841574],"domain_scores_gemma":[0.9922289,0.004343149,0.0004790869,0.001638611,0.0008819912,0.0004282038],"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":[0.001736095,0.0009166934,0.003274372,0.0003751454,0.0001460038,0.0003859217,0.0009260407,0.5074201,0.01947175,0.07025611,0.009944773,0.3851469],"study_design_scores_gemma":[0.00005552001,0.00003571044,0.00006275613,0.000004243853,0.00001039763,0.00002669831,0.00002719271,0.9789839,0.001913303,0.01783681,0.001034414,0.000008998043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007505128,0.0001511781,0.9900292,0.0002564066,0.00005211561,0.00006717564,0.00004537358,0.0007894712,0.001104107],"genre_scores_gemma":[0.6481972,0.0002524272,0.3432172,0.0005694148,0.0004081233,0.0006094968,0.0003634418,0.000277782,0.006104884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006267992,"threshold_uncertainty_score":0.02430427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00796768571783371,"score_gpt":0.2345446226629723,"score_spread":0.2265769369451386,"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."}}