{"id":"W2698640253","doi":"10.1007/s10489-017-0968-2","title":"Batch-normalized Mlpconv-wise supervised pre-training network in network","year":2017,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Training (meteorology); Artificial intelligence; Machine learning","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":[0.0008317584,0.001140141,0.0007920706,0.0005006796,0.0008122567,0.0008761119,0.002483784,0.001481933,0.01120276],"category_scores_gemma":[0.002468731,0.0004857381,0.0006396082,0.0005739623,0.0005312653,0.001255038,0.001250803,0.002135661,0.003908085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126015,"about_ca_system_score_gemma":0.002233386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01339433,"about_ca_topic_score_gemma":0.03193561,"domain_scores_codex":[0.999525,0.00007492308,0.00002414632,0.0001702652,0.0001086206,0.00009696183],"domain_scores_gemma":[0.9990205,0.0001640139,0.00003448001,0.0002508547,0.0004873544,0.0000428043],"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":[0.0004997441,0.0002773506,0.001981042,0.0001924434,0.0001225858,0.0001875914,0.0001132124,0.1926851,0.02527213,0.008664588,0.04349231,0.7265118],"study_design_scores_gemma":[0.00001787055,0.0000805958,0.0009424752,0.00002655933,0.00003267689,0.0000909275,0.00003545699,0.9659662,0.02372742,0.003985515,0.005078339,0.00001599665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08182826,0.00126696,0.8794287,0.0008098513,0.0008921252,0.0003193911,0.001881627,0.01331597,0.02025707],"genre_scores_gemma":[0.5780156,0.000382761,0.3674723,0.0004889083,0.0002109725,0.0004546028,0.004902009,0.0008449923,0.0472278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01339433,"threshold_uncertainty_score":0.03747696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03014919096482449,"score_gpt":0.2847079995776997,"score_spread":0.2545588086128752,"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."}}