{"id":"W4405394884","doi":"10.1016/j.eswa.2024.126017","title":"Learning adversarially robust kernel ensembles with kernel average pooling","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Kernel (algebra); Pooling; Computer science; Artificial intelligence; Multiple kernel learning; Machine learning; Kernel method; Radial basis function kernel; Mathematics; Support vector machine; Discrete mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002083812,0.001513637,0.00143373,0.0004966228,0.0004596628,0.0009477048,0.001585731,0.001407728,0.001013658],"category_scores_gemma":[0.006274096,0.0006793069,0.001078176,0.0004643925,0.001099064,0.002986358,0.002747722,0.002650036,0.0005354709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008494633,"about_ca_system_score_gemma":0.0007522796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001748349,"about_ca_topic_score_gemma":0.001925918,"domain_scores_codex":[0.9989904,0.0003032507,0.00005540009,0.0002501403,0.0002631323,0.0001377073],"domain_scores_gemma":[0.9980803,0.0006602511,0.0002864369,0.0005714837,0.0002809012,0.0001206825],"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":[0.00008469939,0.00004279942,0.0009068353,0.00003802244,0.0001209046,0.00007317995,0.00005245265,0.9400043,0.005236167,0.01230131,0.001521241,0.03961802],"study_design_scores_gemma":[0.000001673777,0.00001728672,0.00006696731,0.000002912975,0.000005105472,0.00001385641,0.000003119657,0.993436,0.001066953,0.005235533,0.000146181,0.000004412843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03613666,0.0003127427,0.9610046,0.0002201398,0.00004680284,0.0000287552,0.00008061056,0.001090795,0.001078909],"genre_scores_gemma":[0.9033821,0.0002680655,0.09318336,0.0002117727,0.00006451353,0.00008010178,0.0003341678,0.0001750368,0.002300968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002083812,"threshold_uncertainty_score":0.01102036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157972025207087,"score_gpt":0.2465815101318245,"score_spread":0.2350017898797536,"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."}}