{"id":"W2780149736","doi":"10.1109/iccv.2017.305","title":"Centered Weight Normalization in Accelerating Training of Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beihang University; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Normalization (sociology); Artificial neural network; Convolutional neural network; Artificial intelligence; Scalability; Deep learning; Norm (philosophy); Deep neural networks; Perceptron; Curvature; Machine learning; Mathematical optimization; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009900183,0.00008351506,0.0001219909,0.00004638175,0.0002221086,0.0001126214,0.0009127738,0.00003516754,0.000009668334],"category_scores_gemma":[0.00003077797,0.00007897324,0.00002795594,0.0001575312,0.00003635617,0.0009569505,0.0002530868,0.00009596298,0.000001280977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001461571,"about_ca_system_score_gemma":0.00000623334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001503751,"about_ca_topic_score_gemma":0.0001609871,"domain_scores_codex":[0.9991716,0.00002457123,0.0002598591,0.0002270845,0.0000989664,0.0002179543],"domain_scores_gemma":[0.999033,0.00005375234,0.0002265935,0.0006046785,0.00004029381,0.00004168825],"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":[0.000006719206,0.00005040185,0.02994606,0.000007739449,0.000005810695,0.000006517063,0.0009862406,0.2821574,0.0009621864,0.1273671,0.00004020784,0.5584636],"study_design_scores_gemma":[0.0002069919,0.00001118587,0.0202639,0.000007740055,8.429042e-7,0.000003165851,0.00001369132,0.9780045,0.0004465318,0.0009187525,0.00004480195,0.00007789781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02679167,0.00002656001,0.9687092,0.0004418173,0.0001440165,0.0001360588,1.987858e-7,0.00005899911,0.003691538],"genre_scores_gemma":[0.9452975,0.000008900621,0.05445905,0.0001238025,0.00006337147,0.00001424774,0.000002390798,0.000005920024,0.00002486525],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9185058,"threshold_uncertainty_score":0.3220435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05577867744953627,"score_gpt":0.2934820380910469,"score_spread":0.2377033606415106,"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."}}