{"id":"W4381852594","doi":"10.1016/j.neucom.2023.126483","title":"MDGAD: Meta domain generalization for distribution drift in anomaly detection","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Anomaly detection; Computer science; Generalization; Artificial intelligence; Concept drift; Merge (version control); Pattern recognition (psychology); Robustness (evolution); Data mining; Test set; Metric (unit); Machine learning; Mathematics; Data stream mining","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":[0.0003824383,0.0001163704,0.0001446206,0.0001726205,0.0002657581,0.00010491,0.0002995512,0.000063811,0.000001400839],"category_scores_gemma":[0.00003021764,0.0001205675,0.0001090372,0.001444316,0.0000127712,0.0002828904,0.0001281867,0.00009292497,0.00001369195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004629109,"about_ca_system_score_gemma":0.00001600572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003040442,"about_ca_topic_score_gemma":0.00001923625,"domain_scores_codex":[0.9988601,0.000059285,0.0002808902,0.0004145464,0.0001227103,0.0002624699],"domain_scores_gemma":[0.9994192,0.00009386789,0.0001143673,0.0002717078,0.00006044911,0.00004037068],"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":[0.00003765609,0.0002438361,0.001991421,0.0001358685,0.0001310862,0.00002071328,0.0006914305,0.04575785,0.1565641,0.3139579,0.003773633,0.4766945],"study_design_scores_gemma":[0.0002953176,0.00009176551,0.01267013,0.0000067662,0.00002084187,0.00001264296,0.00001307709,0.9111015,0.03143594,0.01183039,0.03230232,0.0002193378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1017671,0.00001074004,0.8961299,0.0005805916,0.0001292946,0.0004436323,0.000004945429,0.0008722209,0.0000616402],"genre_scores_gemma":[0.9763196,0.000006831241,0.02299731,0.0001837303,0.0001017513,0.0002871597,0.00003308312,0.00001376678,0.00005682306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8745525,"threshold_uncertainty_score":0.49166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02926753523049013,"score_gpt":0.2756240665815084,"score_spread":0.2463565313510183,"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."}}