{"id":"W3167049326","doi":"10.1109/tkde.2023.3328882","title":"DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Anomaly (physics); Data mining; Data modeling","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":[0.000336086,0.0001718271,0.0001509202,0.0002965219,0.0003315262,0.0001498753,0.001183627,0.00008446008,0.000009373998],"category_scores_gemma":[0.00001397443,0.0001850844,0.00003905367,0.0007114841,0.00001802784,0.001070251,0.00005725256,0.0001631189,0.00005968888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004079521,"about_ca_system_score_gemma":0.00003843066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001338223,"about_ca_topic_score_gemma":0.00004569875,"domain_scores_codex":[0.9986665,0.00001161535,0.0002344904,0.000705694,0.00009711589,0.0002845343],"domain_scores_gemma":[0.9980186,0.0001386841,0.0000388742,0.001642612,0.00004730701,0.0001139881],"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":[0.00002689302,0.0002474704,0.0000109109,0.0002973043,0.0001495351,0.000007874267,0.000588613,0.005900703,0.06146315,0.002287498,0.006098426,0.9229216],"study_design_scores_gemma":[0.0001806605,0.00009270017,0.0001083353,0.00001765719,0.00002598815,0.00001735241,0.00001644587,0.9287847,0.02327662,0.000037578,0.04723144,0.0002104908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009128288,0.00006004719,0.9962047,0.00009836668,0.0006872516,0.0003208499,0.0003281974,0.001328597,0.00005915534],"genre_scores_gemma":[0.9505385,0.0001444363,0.04842865,0.00002567412,0.000147994,0.000221268,0.0001486347,0.00003493699,0.0003098846],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9496257,"threshold_uncertainty_score":0.7547522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05801809301462153,"score_gpt":0.298694577054953,"score_spread":0.2406764840403315,"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."}}