{"id":"W2924907019","doi":"10.24963/ijcai.2019/478","title":"A Principled Approach for Learning Task Similarity in Multitask Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Similarity (geometry); Generalization; Multi-task learning; Task (project management); Set (abstract data type); Artificial neural network; Divergence (linguistics); Perspective (graphical); Feature (linguistics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005889064,0.001579356,0.001823241,0.001453811,0.0008904787,0.001551819,0.003640994,0.002388255,0.002128596],"category_scores_gemma":[0.01420636,0.0009600235,0.001638208,0.001553423,0.002713878,0.003984008,0.005141628,0.004929239,0.0007632876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300463,"about_ca_system_score_gemma":0.001702421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009685165,"about_ca_topic_score_gemma":0.00107409,"domain_scores_codex":[0.9955492,0.001945696,0.0002100274,0.001052655,0.00105843,0.0001840602],"domain_scores_gemma":[0.9949008,0.002939414,0.000445199,0.0009838941,0.0004435632,0.000287052],"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":[0.0003133238,0.0004008844,0.001424056,0.0004744245,0.0003631625,0.0001659451,0.0004062099,0.5481508,0.008444986,0.1885838,0.004673354,0.2465989],"study_design_scores_gemma":[0.00002508059,0.0001139897,0.0001879105,0.0000145035,0.00002029416,0.00005839777,0.00001302546,0.874768,0.001212876,0.1222605,0.001306478,0.00001895275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001658399,0.0001039181,0.9975453,0.0001242512,0.00002293402,0.00004662024,0.00001625031,0.00009976173,0.0003825214],"genre_scores_gemma":[0.3309499,0.0005274022,0.6633739,0.0006209807,0.0003278939,0.0008629455,0.0002338091,0.0002021885,0.002900813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005889064,"threshold_uncertainty_score":0.03114468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01776035050369807,"score_gpt":0.2506778170462925,"score_spread":0.2329174665425944,"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."}}