{"id":"W3035153354","doi":"","title":"Domain Aggregation Networks for Multi-Source Domain Adaptation","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Exploit; Domain (mathematical analysis); Generalization; Domain adaptation; Set (abstract data type); Artificial intelligence; Machine learning; Adaptation (eye); Data mining; Theoretical computer science; Mathematics","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":[0.0021958,0.001210442,0.001060434,0.0009563541,0.000586478,0.0008102429,0.001809226,0.001369848,0.001593873],"category_scores_gemma":[0.005564315,0.000538133,0.0008052422,0.001026208,0.0009196216,0.002628312,0.002217639,0.002881434,0.0007316857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063608,"about_ca_system_score_gemma":0.0007218991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003508458,"about_ca_topic_score_gemma":0.00437594,"domain_scores_codex":[0.9992498,0.0002689509,0.00003067565,0.0002728751,0.0001146287,0.00006306313],"domain_scores_gemma":[0.998161,0.001027551,0.0001144803,0.000354008,0.0002608701,0.00008209973],"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":[0.0002412757,0.0002877948,0.002407458,0.0001487586,0.0001819246,0.0002039983,0.0002379346,0.6396115,0.008479029,0.01680027,0.00692932,0.3244706],"study_design_scores_gemma":[0.000004935274,0.00001770919,0.0001597119,0.00000617004,0.000007883154,0.00002576198,0.00001622983,0.9885105,0.001133291,0.009353068,0.0007584927,0.000006179981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02375613,0.0007213426,0.9724652,0.0002644649,0.00006545916,0.00006232667,0.000112099,0.001038322,0.001514727],"genre_scores_gemma":[0.6871212,0.0006420888,0.3050746,0.0006673678,0.0001535753,0.0003106689,0.001169832,0.0002671583,0.004593533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003508458,"threshold_uncertainty_score":0.01161265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1092256027591612,"score_gpt":0.1986025491779733,"score_spread":0.08937694641881212,"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."}}