{"id":"W2765368425","doi":"10.1007/978-3-319-70096-0_13","title":"Combating Adversarial Inputs Using a Predictive-Estimator Network","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Estimator; Adversarial system; Construct (python library); Artificial intelligence; Machine learning; Feed forward; Process (computing); Mathematics; Statistics; Control engineering","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":[0.001048076,0.0009842423,0.0009397021,0.0005184122,0.0004623194,0.000870506,0.001735476,0.001714451,0.002987522],"category_scores_gemma":[0.003557344,0.0005420669,0.0005867531,0.0004652009,0.001077592,0.001593568,0.002521473,0.002293539,0.0006494208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006849696,"about_ca_system_score_gemma":0.0006426622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002178119,"about_ca_topic_score_gemma":0.002535179,"domain_scores_codex":[0.9994894,0.0001145449,0.00001862883,0.0001500105,0.0001594429,0.00006794993],"domain_scores_gemma":[0.998724,0.000769055,0.00009359148,0.0001614335,0.0002088295,0.00004308989],"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":[0.000131924,0.00005228809,0.0003605203,0.00007002287,0.00004757517,0.0001160303,0.00005317973,0.8388254,0.005545849,0.03040457,0.002346987,0.1220457],"study_design_scores_gemma":[0.000001792845,0.0000199767,0.00003671049,0.000006355291,0.000007708465,0.00002595251,0.000002852187,0.9937104,0.0009685272,0.004876888,0.0003386092,0.000004202037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006990233,0.0003340707,0.989459,0.0001918462,0.00008964763,0.00002206729,0.00003289816,0.0003067995,0.002573411],"genre_scores_gemma":[0.7758413,0.0008082381,0.2067447,0.0004453528,0.000243218,0.0001396041,0.0002168699,0.0001418492,0.01541889],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002987522,"threshold_uncertainty_score":0.009994268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150898241578336,"score_gpt":0.2771464823175662,"score_spread":0.2556374999017828,"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."}}