{"id":"W2002223291","doi":"10.1007/s11390-014-1415-z","title":"Minimizing the Discrepancy Between Source and Target Domains by Learning Adapting Components","year":2014,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Computer science; Independence (probability theory); Embedding; Dimensionality reduction; Range (aeronautics); Feature (linguistics); Reproducing kernel Hilbert space; Domain (mathematical analysis); Theory of computation; Kernel (algebra); Domain adaptation; Feature vector; Curse of dimensionality; Artificial intelligence; Kernel method; Feature selection; Machine learning; Algorithm; Hilbert space; Support vector machine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002373758,0.0001263719,0.0002503744,0.0004048023,0.0007723903,0.0003861686,0.001129965,0.00006604094,9.135247e-7],"category_scores_gemma":[0.0002203473,0.00008723903,0.0000304402,0.0007654011,0.0006783989,0.0006782711,0.0006456701,0.0005048742,0.000002071698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000231782,"about_ca_system_score_gemma":0.00007188941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003226651,"about_ca_topic_score_gemma":2.840357e-7,"domain_scores_codex":[0.9984195,0.0001158191,0.0003664039,0.0002841869,0.0004798512,0.0003342804],"domain_scores_gemma":[0.9986892,0.0002980388,0.0004414048,0.0002192002,0.000225283,0.0001268261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005589196,0.00003356775,0.0339766,0.00001160227,0.00003467327,0.00001114552,0.002516872,0.0005178888,0.008137282,0.03530176,0.0002916305,0.9191614],"study_design_scores_gemma":[0.001449666,0.001078264,0.02097032,0.0001514241,0.00002454424,0.0008714851,0.0007604922,0.794104,0.001625323,0.0127335,0.1657697,0.0004612828],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2994474,0.0002185056,0.6954242,0.004601399,0.0001248232,0.00003696427,8.492843e-8,0.00005037581,0.0000962456],"genre_scores_gemma":[0.867566,0.00002671675,0.1320498,0.0002390909,0.0000913419,6.598772e-7,1.365877e-7,0.000005029389,0.00002126952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9187001,"threshold_uncertainty_score":0.5940681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009523472966358878,"score_gpt":0.2245756298638781,"score_spread":0.2150521568975192,"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."}}