{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001064976,0.0001642781,0.0002376062,0.0001891932,0.0001573557,0.0001944051,0.0005191551,0.00009803152,0.00005816073],"category_scores_gemma":[0.0003570928,0.0001580403,0.00009275592,0.0004811095,0.00001972169,0.0004843562,0.0002179817,0.0005344886,0.0001189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006505793,"about_ca_system_score_gemma":0.00006480501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003156969,"about_ca_topic_score_gemma":0.000009593979,"domain_scores_codex":[0.998256,0.0001854132,0.0002969956,0.0005670153,0.0002590271,0.0004355666],"domain_scores_gemma":[0.9991424,0.000281055,0.0001204561,0.0002863887,0.00007557674,0.00009410494],"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":[0.0000785497,0.0003044682,0.1391262,0.0001629254,0.00004182917,0.000007948231,0.006749159,0.6668221,0.006808622,0.1184468,0.0001175838,0.06133377],"study_design_scores_gemma":[0.001285423,0.0001107732,0.007670674,0.00001124255,0.000002014272,0.00000323926,0.0006198283,0.9665812,0.0001022195,0.000161071,0.02321895,0.0002333831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04052879,0.00003274912,0.9265562,0.000165525,0.0001147133,0.0005667324,2.474144e-7,0.0003492354,0.03168582],"genre_scores_gemma":[0.744242,0.000003677897,0.2476226,0.0001955056,0.00002192701,0.0000434167,0.00001150923,0.0000148023,0.007844633],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7037131,"threshold_uncertainty_score":0.6444696,"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."}}