{"id":"W4409156951","doi":"10.1109/ieeeconf60004.2024.10943038","title":"Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Normalization (sociology); Computer science; Outlier; Minification; Parallel computing; Computer engineering; Artificial intelligence; Algorithm; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009206212,0.0001707485,0.0001487057,0.0001377583,0.00008423535,0.00007866413,0.00008653071,0.00007975855,0.00004103292],"category_scores_gemma":[0.00002327967,0.0001681087,0.00004553114,0.0003409962,0.000009454841,0.0002731461,0.00002116711,0.0001429012,0.00001512863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000803841,"about_ca_system_score_gemma":0.000008794401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000113293,"about_ca_topic_score_gemma":0.000005338201,"domain_scores_codex":[0.999119,0.0000139131,0.0002697634,0.0002528444,0.00009403846,0.0002504609],"domain_scores_gemma":[0.9996512,0.000161822,0.00001895595,0.00009335281,0.00002884129,0.000045857],"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":[0.000009114368,0.00001264681,0.0001602011,0.0002132261,0.000007043853,0.000003933633,0.0002055808,0.9770092,0.001759092,0.0002423121,0.002072057,0.01830562],"study_design_scores_gemma":[0.0002756439,0.00002787825,0.0001027467,0.0001601904,0.000006369426,0.000002788846,0.00009829909,0.9721302,0.02592756,0.00003175631,0.00102769,0.0002089168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2539496,0.0001412205,0.7426094,0.00005682472,0.0008223822,0.0003702594,0.00001394697,0.0008288014,0.001207519],"genre_scores_gemma":[0.9975504,0.00001155016,0.001579976,0.000126721,0.0001824733,0.00001435329,0.0001119881,0.00005796889,0.000364586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7436007,"threshold_uncertainty_score":0.6855274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032450067585958,"score_gpt":0.2531423679178489,"score_spread":0.2328178672419894,"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."}}