{"id":"W3122725470","doi":"10.1049/ell2.12070","title":"Maximising robustness and diversity for improving the deep neural network safety","year":2021,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Robustness (evolution); Artificial neural network; Computer science; Diversity (politics); Artificial intelligence; Reliability engineering; Engineering; Sociology; Biology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005884508,0.0001642734,0.0001654733,0.00002531683,0.001486632,0.000242193,0.0007013631,0.00005303002,0.000003359997],"category_scores_gemma":[0.000128813,0.0001446632,0.0000827511,0.0003115009,0.00006269175,0.0003728647,0.001303098,0.0004493684,7.294052e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372073,"about_ca_system_score_gemma":0.00006233926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085884,"about_ca_topic_score_gemma":0.00006956985,"domain_scores_codex":[0.9983389,0.0001610464,0.0001642035,0.0004655592,0.0002124997,0.0006577882],"domain_scores_gemma":[0.9989412,0.0003981366,0.000110864,0.0004327746,0.00005984127,0.00005714648],"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":[0.00002668388,0.000008149643,0.00157936,0.00002252922,0.00003824288,0.00002702186,0.0004868362,0.9269743,0.001075517,0.01529094,0.0002633358,0.05420708],"study_design_scores_gemma":[0.0004448365,0.00002705311,0.001108455,0.00000592048,0.0000299659,0.00005489759,0.00002775412,0.9957156,0.000140934,0.0007575228,0.001471894,0.0002152302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0477274,0.0009354225,0.9366838,0.01388975,0.0004663275,0.0001642395,4.728345e-7,0.00009892005,0.00003365332],"genre_scores_gemma":[0.9155018,0.00002386204,0.07834886,0.005571921,0.0004647516,0.000009086846,0.000007020133,0.00002509233,0.00004764068],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8677744,"threshold_uncertainty_score":0.9998133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009057066319669875,"score_gpt":0.2147684085609678,"score_spread":0.205711342241298,"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."}}