{"id":"W2998437255","doi":"","title":"Understanding BatchNorm in Ternary Training","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ternary operation; Artificial neural network; Binary number; Computer science; Function (biology); Training (meteorology); Artificial intelligence; Mathematics; Arithmetic; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003218742,0.0007715005,0.0006875251,0.0004750188,0.0005910202,0.001924283,0.001680142,0.001412486,0.003628144],"category_scores_gemma":[0.01489258,0.0005060119,0.0004767898,0.0006006695,0.002407741,0.004497274,0.002049636,0.002928161,0.0007320058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225691,"about_ca_system_score_gemma":0.001196813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00373676,"about_ca_topic_score_gemma":0.003699021,"domain_scores_codex":[0.9986331,0.0004837797,0.00008572312,0.0002789199,0.0003955716,0.0001229277],"domain_scores_gemma":[0.9956175,0.002725703,0.000329965,0.0006415325,0.0005530888,0.0001322616],"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":[0.0005375075,0.0001299842,0.002887641,0.0003093023,0.00006384493,0.0003443396,0.0006312709,0.2915656,0.01461021,0.3854585,0.007675481,0.2957863],"study_design_scores_gemma":[0.00001890412,0.00006071635,0.000415546,0.0000425803,0.00001332985,0.00009338664,0.00003483984,0.8454807,0.008255226,0.1421701,0.003395769,0.00001875676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0211733,0.0007285298,0.971225,0.001089072,0.0001346521,0.00004536948,0.0000980307,0.0005793226,0.00492676],"genre_scores_gemma":[0.564279,0.001457686,0.4208747,0.001340848,0.0004065796,0.0003135746,0.0004484715,0.0005626027,0.01031654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00373676,"threshold_uncertainty_score":0.01702255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04719880229903626,"score_gpt":0.2811962168183445,"score_spread":0.2339974145193082,"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."}}