{"id":"W2951025196","doi":"","title":"Learning Algorithms for Active Learning","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Microsoft (Canada)","funders":"","keywords":"MovieLens; Heuristic; Computer science; Artificial intelligence; Machine learning; Construct (python library); Selection (genetic algorithm); Representation (politics); Model selection; Algorithm; Training set; Active learning (machine learning); Recommender system; Collaborative filtering","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":[0.00675932,0.002511309,0.002642573,0.002619644,0.00110072,0.004721615,0.005889712,0.003792472,0.01150707],"category_scores_gemma":[0.02813838,0.001338094,0.002226555,0.003664591,0.002501585,0.006367368,0.004819088,0.007016813,0.006022591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001817984,"about_ca_system_score_gemma":0.001964147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001940331,"about_ca_topic_score_gemma":0.001866973,"domain_scores_codex":[0.9942287,0.002768627,0.0003916882,0.001097827,0.001250243,0.0002628209],"domain_scores_gemma":[0.9867778,0.0095128,0.0005320979,0.001727274,0.001175007,0.0002749789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007638871,0.0001736308,0.000927404,0.0005196666,0.0002261157,0.00009123718,0.0001684815,0.1606322,0.0005509347,0.5856603,0.02052595,0.2304478],"study_design_scores_gemma":[0.00004048906,0.00002965369,0.00006660751,0.00006972782,0.00002326987,0.00005911727,0.00001893313,0.470939,0.0003081845,0.5157132,0.01271488,0.00001688369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003183744,0.000825163,0.9956188,0.0004305408,0.0001134917,0.00005646303,0.0001220521,0.000360123,0.002155033],"genre_scores_gemma":[0.08177865,0.003925791,0.8972024,0.001281633,0.00130554,0.001822301,0.00156713,0.00059794,0.01051859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01150707,"threshold_uncertainty_score":0.03849494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07412385670808344,"score_gpt":0.2285695936773584,"score_spread":0.1544457369692749,"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."}}