{"id":"W3217600415","doi":"10.48550/arxiv.2012.12896","title":"How Does a Neural Network's Architecture Impact Its Robustness to Noisy\\n Labels?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Network architecture; Architecture; Predictive power; Artificial intelligence; Artificial neural network; Machine learning; Deep neural networks","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":[0.02223727,0.001686463,0.001130757,0.002005499,0.001490216,0.004633543,0.002258379,0.003659229,0.001894537],"category_scores_gemma":[0.116461,0.0008120717,0.0008801025,0.001095643,0.004781964,0.009099484,0.003461056,0.004583383,0.001402046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001847712,"about_ca_system_score_gemma":0.001135613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004536749,"about_ca_topic_score_gemma":0.004796004,"domain_scores_codex":[0.9888141,0.005095981,0.0006468268,0.00272962,0.00205618,0.0006573877],"domain_scores_gemma":[0.929226,0.0473737,0.006047176,0.01129583,0.004875795,0.001181503],"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":[0.001240003,0.0003183555,0.1136987,0.000714335,0.001211016,0.000351247,0.001039911,0.572386,0.01504857,0.02988624,0.008034674,0.256071],"study_design_scores_gemma":[0.00006439853,0.0004958929,0.02113846,0.0003318238,0.0002618768,0.0003503824,0.0006245521,0.8395934,0.02001858,0.1126422,0.004340058,0.000138482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5264512,0.004928628,0.4276762,0.01648386,0.0006837905,0.0002174731,0.001572338,0.002929513,0.01905705],"genre_scores_gemma":[0.967501,0.0007655909,0.02759775,0.001033819,0.0002329555,0.0000819713,0.0009624141,0.0003323419,0.001492114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02223727,"threshold_uncertainty_score":0.1176034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06389736011308098,"score_gpt":0.2098063504554722,"score_spread":0.1459089903423912,"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."}}