{"id":"W2999250053","doi":"10.48550/arxiv.2001.03674","title":"Semi-supervised Anomaly Detection using AutoEncoders","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Artificial intelligence; Computer science; Pattern recognition (psychology); Segmentation; Outlier; Residual; Task (project management); Process (computing); Anomaly (physics); Automation; Computer vision; Engineering; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000109103,0.0002916884,0.0002690322,0.0002298853,0.0003000286,0.0001560676,0.001391452,0.0003163384,0.00002398053],"category_scores_gemma":[0.00001279253,0.0003705553,0.0002562016,0.000950714,0.00007600847,0.0003698765,0.001388468,0.0005761759,0.00006126821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002963539,"about_ca_system_score_gemma":0.0001763691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003551984,"about_ca_topic_score_gemma":0.00003130254,"domain_scores_codex":[0.9981704,0.00008511981,0.0002150178,0.001163924,0.00008712017,0.0002783703],"domain_scores_gemma":[0.998374,0.00003132535,0.0002379607,0.001036889,0.0001271852,0.0001926375],"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":[0.00005436858,0.0002242433,0.001747359,0.0002301461,0.0002639942,0.0002985016,0.0006236052,0.8309184,0.01062308,0.147019,0.0003586096,0.007638775],"study_design_scores_gemma":[0.000149781,0.00004329451,0.0003160548,0.00002546299,0.0000483701,0.000008725186,0.00003968341,0.9716811,0.002890368,0.02367357,0.0007448827,0.0003786777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172104,0.00001710916,0.8798929,0.0001554097,0.0002208975,0.0003246456,0.000008426347,0.001067503,0.001102736],"genre_scores_gemma":[0.9745107,0.00004962269,0.02490097,0.0001663365,0.00008610541,0.000003225262,0.000005437354,0.00002216984,0.0002554536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8573003,"threshold_uncertainty_score":0.9998747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026782450392616,"score_gpt":0.2006839415978423,"score_spread":0.09800569655858073,"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."}}