{"id":"W3212940494","doi":"10.48550/arxiv.2111.06549","title":"Bi-Discriminator Class-Conditional Tabular GAN","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Discriminator; Discriminative model; Computer science; Generator (circuit theory); Preprocessor; Benchmarking; Binary number; Metric (unit); Class (philosophy); Term (time); Artificial intelligence; Data mining; Machine learning; Algorithm; Mathematics; Engineering; Detector","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001078099,0.0009102356,0.0006983112,0.0004844878,0.0002109305,0.0009440292,0.001671715,0.0008577773,0.005518114],"category_scores_gemma":[0.002932134,0.0003238432,0.0006048837,0.0007129017,0.0005966398,0.001173416,0.00129452,0.001862182,0.002039983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007318237,"about_ca_system_score_gemma":0.0005510098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001058692,"about_ca_topic_score_gemma":0.002706856,"domain_scores_codex":[0.9994833,0.0001697939,0.00001906739,0.0001574809,0.0001171699,0.00005319856],"domain_scores_gemma":[0.9990727,0.0004283457,0.000053386,0.0002464195,0.0001553502,0.00004387084],"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":[0.0004028312,0.0001628817,0.002908075,0.0001981649,0.0001063333,0.0002021319,0.0001291347,0.583597,0.02692732,0.05183303,0.02426711,0.309266],"study_design_scores_gemma":[0.00001063818,0.00002765889,0.0002593551,0.00001033258,0.000007134541,0.00005163263,0.000006703866,0.9818602,0.003590784,0.01210742,0.002059035,0.00000908782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01490538,0.0002623261,0.9752814,0.0002185694,0.0000896961,0.00006905736,0.001042932,0.003616984,0.004513639],"genre_scores_gemma":[0.5459311,0.0002843671,0.431484,0.001061578,0.0001014377,0.0004588998,0.00657321,0.001097157,0.01300818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005518114,"threshold_uncertainty_score":0.01845992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05563183401997562,"score_gpt":0.1892666705818361,"score_spread":0.1336348365618605,"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."}}