{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001742494,0.00006614001,0.00005973181,0.0001106429,0.0005633362,0.0000661401,0.0003337456,0.00001637956,0.00006628827],"category_scores_gemma":[0.000002809558,0.00005968967,0.00002750932,0.0007463979,0.0000105962,0.0002393444,0.000285177,0.0001696413,0.00001091181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001016969,"about_ca_system_score_gemma":0.00003683333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000697335,"about_ca_topic_score_gemma":0.00003551652,"domain_scores_codex":[0.9992487,0.00002986891,0.0001127647,0.0002968819,0.0001812442,0.0001305035],"domain_scores_gemma":[0.9994494,0.00001248439,0.00005551685,0.0004136573,0.00003203527,0.00003693864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003460309,0.000316022,0.005729767,0.00002626273,0.00007585489,0.00004611092,0.002675292,0.00665806,0.0640085,0.7347423,0.02929454,0.1563927],"study_design_scores_gemma":[0.0007301831,0.001698271,0.003405951,0.00002168537,0.00001814847,0.001186904,0.0005514463,0.108275,0.2001176,0.01318447,0.6696596,0.001150853],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02365524,0.000004062619,0.9587296,0.002000466,0.00004432218,0.0001328424,2.683929e-7,0.001182493,0.01425073],"genre_scores_gemma":[0.8631541,7.963862e-7,0.1344753,0.0005068415,0.00002500598,0.0001847387,6.146233e-7,0.000006354467,0.001646252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8394988,"threshold_uncertainty_score":0.4332784,"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."}}