{"id":"W4281388959","doi":"10.1016/j.patrec.2022.05.023","title":"Bi-discriminator GAN for tabular data synthesis","year":2022,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; University of Windsor; Université du Québec à Montréal","funders":"","keywords":"Discriminator; Discriminative model; Computer science; Generator (circuit theory); Binary number; Preprocessor; Benchmarking; Metric (unit); Term (time); Data mining; Artificial intelligence; Scheme (mathematics); Algorithm; Pattern recognition (psychology); Machine learning; Mathematics; Detector; Engineering; Power (physics)","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":[0.0003051616,0.0001097893,0.000106596,0.0001307423,0.0004653759,0.00009657948,0.001202303,0.00002121509,0.0002425838],"category_scores_gemma":[0.00002577877,0.000123487,0.00006663564,0.0002315128,0.00002359142,0.0003262222,0.000420872,0.0001181528,0.00005674219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005305754,"about_ca_system_score_gemma":0.00001617814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003702476,"about_ca_topic_score_gemma":0.000003337814,"domain_scores_codex":[0.9988155,0.00006791043,0.000199431,0.0005143122,0.0001945176,0.0002083138],"domain_scores_gemma":[0.9988195,0.0001258745,0.0001156362,0.0008500192,0.00003333353,0.00005559028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006601915,0.0001232906,0.0002144261,0.00003341982,0.00003162536,0.00000706394,0.000119393,0.00001799686,0.008695138,0.0002257382,0.06042813,0.9300972],"study_design_scores_gemma":[0.001067729,0.0003235887,0.001536096,0.00005955321,0.000181005,0.0001537122,0.0004985019,0.1647623,0.1090504,0.007416831,0.7131397,0.001810556],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01275278,0.00001208693,0.9732724,0.01233243,0.0001933879,0.0004344259,0.0005195152,0.0003477163,0.0001353066],"genre_scores_gemma":[0.9433522,0.00000923624,0.04023073,0.01290067,0.0001536507,0.002870498,0.000404844,0.00002955104,0.000048681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9330416,"threshold_uncertainty_score":0.5035653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06906643072439422,"score_gpt":0.2715178054476694,"score_spread":0.2024513747232752,"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."}}