{"id":"W2973077827","doi":"10.1109/tnnls.2019.2935608","title":"Domain Adaptation With Neural Embedding Matching","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Postdoctoral Program for Innovative Talents; Canada Research Chairs; Natural Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Embedding; Computer science; Artificial intelligence; Matching (statistics); Representation (politics); Artificial neural network; Domain (mathematical analysis); Domain adaptation; Feature learning; Generalization; Exploit; Benchmark (surveying); Machine learning; Theoretical computer science; Pattern recognition (psychology); Mathematics; Classifier (UML)","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":[0.0004337785,0.0002431951,0.0002614289,0.0001585829,0.0005940566,0.0005456905,0.0002280469,0.00009503811,0.000009541069],"category_scores_gemma":[0.000001945938,0.0002059866,0.00006842941,0.0003618624,0.00003342878,0.0006579452,0.000003833829,0.0008956764,0.00001941925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003695939,"about_ca_system_score_gemma":0.00001894851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006234088,"about_ca_topic_score_gemma":0.00001125962,"domain_scores_codex":[0.9981328,0.0003558715,0.0002948509,0.0004981303,0.0003328157,0.0003854919],"domain_scores_gemma":[0.9990953,0.0002766777,0.0001818877,0.0002526827,0.00005361173,0.0001398],"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":[0.00003883731,0.00001493284,0.0001166608,0.00002205352,0.00001985982,0.000008877142,0.001672421,0.9820681,0.0001565791,0.0009211971,0.000004262514,0.01495621],"study_design_scores_gemma":[0.0005759028,0.0002893334,0.0002057292,0.0001034094,0.000009654997,0.00009910577,0.001485864,0.9964542,0.000005275552,0.00001371255,0.0004936954,0.0002641122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1832266,0.0001014571,0.8149263,0.0001243955,0.0007772213,0.0002603953,4.070204e-7,0.0002858832,0.0002972676],"genre_scores_gemma":[0.9929628,0.00001502299,0.005258481,0.00016704,0.00007177375,0.00002625779,0.000001887822,0.00003214899,0.001464559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8097362,"threshold_uncertainty_score":0.8399889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01161604965998904,"score_gpt":0.2226185692590597,"score_spread":0.2110025195990707,"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."}}