{"id":"W2964416840","doi":"10.24963/ijcai.2019/246","title":"Augmenting Transfer Learning with Semantic Reasoning","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transfer of learning; Exploit; Semantics (computer science); Artificial intelligence; Semantic Web; Quality (philosophy); Transfer (computing); Semantic computing; Knowledge transfer; Machine learning; Natural language processing; Knowledge management; Programming language","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":[0.003839203,0.002008049,0.001768772,0.001598329,0.0007425596,0.001758577,0.002967106,0.002545455,0.003050364],"category_scores_gemma":[0.01545262,0.0006072241,0.001495434,0.001568959,0.002231799,0.006906735,0.006127182,0.003623103,0.001091653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014315,"about_ca_system_score_gemma":0.001229962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002406398,"about_ca_topic_score_gemma":0.001883678,"domain_scores_codex":[0.9978008,0.0008163142,0.0001118867,0.0006317382,0.0004780795,0.0001611883],"domain_scores_gemma":[0.9943112,0.003223354,0.0002950021,0.001447279,0.0005712053,0.0001519189],"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":[0.0002746513,0.0005181234,0.001769781,0.0002791763,0.0003038269,0.0002097845,0.000289175,0.5963289,0.00691489,0.04209585,0.003786718,0.3472292],"study_design_scores_gemma":[0.00001268034,0.00004518553,0.0001341008,0.000008889601,0.00001541199,0.00001630655,0.00001623451,0.9347681,0.001816945,0.06258695,0.0005673208,0.00001183719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0174216,0.0003588519,0.979134,0.0003712235,0.00009092544,0.00005025941,0.00009368424,0.00114445,0.001335069],"genre_scores_gemma":[0.7720798,0.0005177349,0.2220039,0.0005037512,0.0002732669,0.0002050684,0.0007808876,0.000288256,0.003347327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003839203,"threshold_uncertainty_score":0.02030391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620833704284038,"score_gpt":0.2351116021474014,"score_spread":0.218903265104561,"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."}}