{"id":"W2972873299","doi":"10.48550/arxiv.1909.05352","title":"Domain Aggregation Networks for Multi-Source Domain Adaptation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Domain adaptation; Domain (mathematical analysis); Adaptation (eye); Computer science; Artificial intelligence; Psychology; Mathematics; Neuroscience","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":[0.002198705,0.001216096,0.00107442,0.000967549,0.0005944408,0.0008469413,0.001846974,0.001424877,0.001645087],"category_scores_gemma":[0.005855251,0.0005512841,0.0008238837,0.00105035,0.0009460584,0.002673844,0.002294227,0.002930044,0.0007557869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071579,"about_ca_system_score_gemma":0.000715077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003320586,"about_ca_topic_score_gemma":0.004109004,"domain_scores_codex":[0.9992131,0.0002848725,0.00003213106,0.0002861184,0.0001192101,0.00006447559],"domain_scores_gemma":[0.9980964,0.001061715,0.0001193804,0.0003755572,0.0002611043,0.00008597729],"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":[0.0002462062,0.000281794,0.002398953,0.0001569984,0.0001869594,0.0002097861,0.0002468681,0.6412521,0.008735102,0.01833807,0.006889994,0.3210571],"study_design_scores_gemma":[0.00000490465,0.00001717235,0.0001613721,0.000006275411,0.000007853067,0.00002608237,0.00001611305,0.98769,0.001155551,0.01013691,0.0007715977,0.000006152572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02208182,0.0006750294,0.9743614,0.0002566111,0.00006189059,0.00005927042,0.000109054,0.0009842417,0.001410756],"genre_scores_gemma":[0.6762642,0.0006570322,0.3157018,0.0006560949,0.0001597119,0.0003246751,0.001206105,0.0002788245,0.004751732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003320586,"threshold_uncertainty_score":0.01162803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09661974135682251,"score_gpt":0.2106112194558157,"score_spread":0.1139914780989932,"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."}}