{"id":"W3092397959","doi":"10.48550/arxiv.2010.02508","title":"Adversarial Boot Camp: label free certified robustness in one epoch","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Certification; Computer science; Robustness (evolution); Machine learning; Artificial intelligence; Retraining; Adversarial system; Equivalence (formal languages); Mathematics","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":[0.006036397,0.001414931,0.001516356,0.0006952924,0.001132875,0.001924558,0.003115986,0.003257773,0.003773644],"category_scores_gemma":[0.02879808,0.0008157301,0.00138618,0.0005029629,0.004666106,0.004878873,0.007230063,0.0061492,0.001173821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001728179,"about_ca_system_score_gemma":0.002459379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340193,"about_ca_topic_score_gemma":0.001453834,"domain_scores_codex":[0.995895,0.00144306,0.0001724925,0.0008534794,0.001152066,0.0004838406],"domain_scores_gemma":[0.9808801,0.009613069,0.001247577,0.006776277,0.001018748,0.0004643321],"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":[0.0006213839,0.0001693376,0.001390618,0.000129884,0.0001262807,0.0003674822,0.0002166882,0.8377724,0.009598069,0.09300905,0.007330537,0.04926834],"study_design_scores_gemma":[0.00002799806,0.00008714416,0.0001125663,0.0000206707,0.00001075871,0.00006740378,0.00001670172,0.9308708,0.003982638,0.06392472,0.0008595978,0.00001901229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04471052,0.0003520551,0.9428519,0.001562184,0.0001732745,0.0001487723,0.0002270203,0.004084486,0.005889714],"genre_scores_gemma":[0.9061499,0.0001541662,0.08818232,0.000621875,0.0001204579,0.000169676,0.0003248293,0.0006608532,0.003615869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006036397,"threshold_uncertainty_score":0.03192395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12933512399401,"score_gpt":0.2149717276768977,"score_spread":0.08563660368288764,"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."}}