{"id":"W3101271453","doi":"10.1109/issre5003.2020.00047","title":"TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications","year":2020,"lang":"en","type":"article","venue":"","topic":"Radiation Effects in Electronics","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault injection; Resilience (materials science); Computer science; Fault tolerance; Reliability (semiconductor); Domain (mathematical analysis); Fault (geology); Software; Embedded system; Software fault tolerance; Software engineering; Reliability engineering; Artificial intelligence; Distributed computing; Computer engineering; Machine learning; Programming language; Engineering","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":[0.00004548835,0.0001112119,0.0001132407,0.00004205644,0.00006979916,0.00003191735,0.0001115199,0.0001133284,0.00006128282],"category_scores_gemma":[0.0001016538,0.0001148806,0.00005680784,0.0003491187,0.00001125032,0.00008839741,0.000007062209,0.0001748137,0.0001516684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007432016,"about_ca_system_score_gemma":0.00001566214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001637275,"about_ca_topic_score_gemma":0.000001649704,"domain_scores_codex":[0.9993697,0.000007530636,0.0001449432,0.0001662494,0.00008629314,0.0002252196],"domain_scores_gemma":[0.9995351,0.000149471,0.00001848064,0.0001637974,0.00004763105,0.00008546802],"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":[0.000006314149,0.00001077345,0.00002886625,0.00007804812,0.0000369854,9.731771e-8,0.0001344669,0.9154701,0.001414862,0.05512666,0.009046548,0.01864628],"study_design_scores_gemma":[0.0001673386,0.00009277706,0.00004600028,0.000004100206,0.00001585137,0.00000213131,0.00003956294,0.8561124,0.004929044,0.007653696,0.1307695,0.0001676049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001953852,0.000171385,0.9909239,0.000790089,0.0001156673,0.0007152648,0.0000119688,0.001494154,0.003823667],"genre_scores_gemma":[0.8735243,0.00003621434,0.1223085,0.001526492,0.0008442228,0.001223054,0.00003369822,0.00009947905,0.0004040709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8715704,"threshold_uncertainty_score":0.4684694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140892867903819,"score_gpt":0.248438957570783,"score_spread":0.2343496707804011,"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."}}