{"id":"W2951720195","doi":"","title":"Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Machine learning; Artificial neural network; Pattern recognition (psychology)","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":[0.006331903,0.001363147,0.001069859,0.001479285,0.0007107211,0.002132117,0.002619474,0.001119491,0.003046278],"category_scores_gemma":[0.02096765,0.000486672,0.0009769138,0.001871044,0.001548997,0.004765322,0.002247278,0.002079122,0.001711144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001395827,"about_ca_system_score_gemma":0.001361152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004262355,"about_ca_topic_score_gemma":0.004131364,"domain_scores_codex":[0.9968816,0.0008688361,0.0001664996,0.000904928,0.0009784716,0.0001996051],"domain_scores_gemma":[0.9938852,0.001794585,0.000500104,0.002564373,0.00113725,0.000118393],"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":[0.0004947464,0.0001615553,0.002935312,0.0003182379,0.0002943357,0.00009990021,0.0004144205,0.1405659,0.0238326,0.06184286,0.008377929,0.7606621],"study_design_scores_gemma":[0.00006780557,0.0002924396,0.003588795,0.0001286845,0.0002605194,0.0003090631,0.0002228626,0.8109603,0.07354505,0.0805543,0.02995103,0.0001191733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03145993,0.002161399,0.9516935,0.0004068169,0.0002641544,0.0001454881,0.000337131,0.00455748,0.008974126],"genre_scores_gemma":[0.4355519,0.002654162,0.545688,0.0004285885,0.0002891119,0.000384378,0.001507486,0.002516647,0.0109796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006331903,"threshold_uncertainty_score":0.03348672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0711780634921327,"score_gpt":0.209903069066744,"score_spread":0.1387250055746113,"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."}}