{"id":"W4402591948","doi":"10.36227/techrxiv.172668728.85275057/v1","title":"Improving Adversarial Robustness of Conjugate Neural Networks with Guided Diversity","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adversarial system; Robustness (evolution); Conjugate; Diversity (politics); Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Mathematics; Political science; Biology; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001888964,0.001079154,0.00079944,0.0005323691,0.0004121432,0.0007696588,0.001031304,0.00103996,0.001275449],"category_scores_gemma":[0.007421088,0.0004310565,0.0005246659,0.0003184965,0.001647114,0.00189979,0.002933476,0.001656347,0.0002965576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006685618,"about_ca_system_score_gemma":0.0007041895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001156004,"about_ca_topic_score_gemma":0.001222938,"domain_scores_codex":[0.9991226,0.0003187054,0.00004583857,0.0001750443,0.0002235501,0.000114266],"domain_scores_gemma":[0.9973137,0.001472376,0.0003325615,0.0004242256,0.0003430933,0.0001140552],"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":[0.0001340523,0.00003369559,0.0009330178,0.0000377825,0.00005972627,0.0000770619,0.00005131109,0.9505899,0.00718037,0.01279717,0.0006485176,0.02745739],"study_design_scores_gemma":[0.000004315735,0.00003047202,0.00006806626,0.000003247254,0.000004035395,0.00001624125,0.000003390478,0.9936081,0.002056883,0.004041683,0.000158915,0.000004620333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1061521,0.0004630643,0.8881133,0.0003978068,0.00009690908,0.00004365599,0.00005484023,0.0008008893,0.003877563],"genre_scores_gemma":[0.9584143,0.0001464133,0.03932782,0.0001668163,0.00003790794,0.00003569752,0.00005943832,0.00007764049,0.001733898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001888964,"threshold_uncertainty_score":0.009989917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809941721385828,"score_gpt":0.2484768591472456,"score_spread":0.2303774419333873,"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."}}