{"id":"W4382467791","doi":"10.1609/aaai.v37i7.26012","title":"The Effect of Diversity in Meta-Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Task (project management); Diversity (politics); Computer science; Distribution (mathematics); Cognitive psychology; Empirical evidence; Machine learning; Artificial intelligence; Psychology; Mathematics; Epistemology; Engineering; Sociology","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":[0.0205633,0.001291624,0.001323087,0.001279732,0.001379356,0.003022903,0.001764177,0.002697089,0.001598399],"category_scores_gemma":[0.1049333,0.0005650969,0.0007763903,0.0007361093,0.002840165,0.008254057,0.005362401,0.004161032,0.0004138574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009331689,"about_ca_system_score_gemma":0.0007596724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009148438,"about_ca_topic_score_gemma":0.0009983834,"domain_scores_codex":[0.9888216,0.007043626,0.0005065076,0.001920095,0.00126693,0.000441248],"domain_scores_gemma":[0.8659343,0.114126,0.003579672,0.01065927,0.002852421,0.002848308],"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":[0.006747642,0.002598471,0.1002909,0.001492546,0.002613098,0.0007503477,0.003856447,0.4292871,0.0397624,0.03327208,0.004093333,0.3752356],"study_design_scores_gemma":[0.000909407,0.0067702,0.03525176,0.0004502902,0.00100698,0.001134446,0.001287457,0.7665305,0.02869738,0.1512809,0.006423246,0.0002573857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.821115,0.006804678,0.1546639,0.003470996,0.0002553993,0.0002497867,0.000220423,0.0007077052,0.01251219],"genre_scores_gemma":[0.9798585,0.0003748019,0.01849645,0.0003778535,0.0001250201,0.00006510602,0.0001446549,0.00007086061,0.0004867045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0205633,"threshold_uncertainty_score":0.1087503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1441955617943949,"score_gpt":0.3180096653225486,"score_spread":0.1738141035281537,"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."}}