{"id":"W2963096987","doi":"","title":"A closer look at memorization in deep networks","year":2017,"lang":"en","type":"article","venue":"Jagiellonian University Repository (Jagiellonian University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":654,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Canadian Institute for Advanced Research; McGill University; Université de Montréal; Concordia University","funders":"Samsung; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology; Canadian Institute for Advanced Research","keywords":"Memorization; Deep neural networks; Computer science; Artificial intelligence; Generalization; Deep learning; Robustness (evolution); Regularization (linguistics); Artificial neural network; Machine learning; Adversarial system; Noise (video); Mathematics; Cognitive psychology; Psychology","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":[0.002543545,0.0007706702,0.0006651084,0.001097958,0.0006081768,0.002163223,0.00169985,0.001204752,0.004687249],"category_scores_gemma":[0.01868637,0.000453367,0.0007231068,0.0005845423,0.003581737,0.009362306,0.002589638,0.00406819,0.0003588406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373043,"about_ca_system_score_gemma":0.0004634382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001160617,"about_ca_topic_score_gemma":0.0009420256,"domain_scores_codex":[0.9989477,0.0003387314,0.00004979781,0.0002319373,0.0002753974,0.0001564993],"domain_scores_gemma":[0.9914241,0.004904456,0.001097168,0.001658876,0.0006122194,0.0003031958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004822195,0.0003088928,0.007886223,0.000603525,0.0002268379,0.0006335609,0.00232077,0.3523358,0.04811896,0.4395513,0.005027211,0.1425047],"study_design_scores_gemma":[0.00003713558,0.000551046,0.005793248,0.0002749919,0.00006175388,0.0005095727,0.000450157,0.5140811,0.02767316,0.4400003,0.01047743,0.00009024544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2759846,0.005454515,0.6735771,0.01143119,0.0003889817,0.0001295209,0.0003409044,0.001055368,0.03163786],"genre_scores_gemma":[0.9580191,0.00133706,0.03444114,0.0006051321,0.0002174584,0.00005779586,0.00009200166,0.0001614535,0.00506886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004687249,"threshold_uncertainty_score":0.01568043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006018646023883206,"score_gpt":0.1883984567121577,"score_spread":0.1823798106882745,"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."}}