{"id":"W4409347793","doi":"10.1609/aaai.v39i18.34058","title":"Till the Layers Collapse: Compressing a Deep Neural Network Through the Lenses of Batch Normalization Layers.","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"HORIZON EUROPE Framework Programme; Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; China Scholarship Council; European Commission; Agence Nationale de la Recherche; Institute for Catastrophic Loss Reduction","keywords":"Normalization (sociology); Artificial neural network; Geology; Materials science; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001405896,0.002109676,0.0009933,0.001109362,0.0008613113,0.001796054,0.002677874,0.001226419,0.006062821],"category_scores_gemma":[0.007816244,0.0008950027,0.001322965,0.001366716,0.001458821,0.005249199,0.003043036,0.004007628,0.002230945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591261,"about_ca_system_score_gemma":0.002609538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01236494,"about_ca_topic_score_gemma":0.01718008,"domain_scores_codex":[0.9991587,0.000128052,0.00006078696,0.0001992411,0.0003516803,0.0001015274],"domain_scores_gemma":[0.9985422,0.0004142182,0.0001592406,0.0005081762,0.0002714143,0.0001047306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009954168,0.0002118334,0.002781292,0.0004180836,0.0004362091,0.0005071347,0.0005133911,0.1119528,0.05693099,0.05108707,0.05388876,0.720277],"study_design_scores_gemma":[0.00007459832,0.0001518758,0.0008553336,0.00007290682,0.0001271225,0.0002780215,0.0001221967,0.8729135,0.06203528,0.03863375,0.02466053,0.00007490772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01827315,0.001280324,0.9655175,0.0008460659,0.0005307512,0.0001714387,0.0007442512,0.009125158,0.00351142],"genre_scores_gemma":[0.2067094,0.001327335,0.7703252,0.00142726,0.0004012225,0.0004243339,0.00289634,0.002246519,0.01424233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01236494,"threshold_uncertainty_score":0.02458596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05289706677164575,"score_gpt":0.2983619473187461,"score_spread":0.2454648805471004,"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."}}