{"id":"W4313563643","doi":"10.1145/3551349.3556932","title":"Repairing Failure-inducing Inputs with Input Reflection","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation; Bộ Giáo dục và Ðào tạo; National Research Foundation Singapore; National University of Singapore; Cisco Systems","keywords":"Deep neural networks; Computer science; Reflection (computer programming); Artificial neural network; Training set; Training (meteorology); Artificial intelligence; Production (economics); Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002829591,0.002166911,0.001244859,0.0008988334,0.0004777289,0.001061949,0.0025419,0.001337698,0.004545961],"category_scores_gemma":[0.02022528,0.0006602124,0.0009675924,0.0006104655,0.001137755,0.002168525,0.002823588,0.002399001,0.001601522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008164941,"about_ca_system_score_gemma":0.001547437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002131215,"about_ca_topic_score_gemma":0.003263653,"domain_scores_codex":[0.9981592,0.0003137382,0.000154865,0.0005282641,0.0006140657,0.0002299176],"domain_scores_gemma":[0.9911452,0.002783251,0.0007140339,0.003184759,0.001946528,0.0002261638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008424236,0.0002425163,0.01068907,0.0005091839,0.0001724584,0.0009591057,0.0003041465,0.6911683,0.0270676,0.01134908,0.01531504,0.2413811],"study_design_scores_gemma":[0.00002359797,0.0001820571,0.001381987,0.00007548551,0.00004664203,0.0001645307,0.00006824784,0.9586036,0.0220166,0.01459838,0.002809596,0.00002927738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0833092,0.0003766702,0.9019676,0.0005357781,0.0004306511,0.0001513255,0.000902454,0.008529348,0.003797061],"genre_scores_gemma":[0.8294423,0.0002580775,0.1624537,0.0004544927,0.00009950458,0.0002465843,0.002573167,0.0006864029,0.003785913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004545961,"threshold_uncertainty_score":0.01520777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202713673948786,"score_gpt":0.2361818366102402,"score_spread":0.2241546998707523,"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."}}