{"id":"W2123847705","doi":"10.1109/tnn.2011.2163169","title":"A New Formulation for Feedforward Neural Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Feedforward neural network; Regularization (linguistics); Stochastic neural network; Artificial intelligence; Backpropagation; Time delay neural network; Types of artificial neural networks; Generalization; Measure (data warehouse); Probabilistic neural network; Feed forward; Machine learning; Algorithm; Mathematics; Data mining","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":[0.001421099,0.001215324,0.0006869579,0.0007375467,0.0003587426,0.001293282,0.001597228,0.001663288,0.003755886],"category_scores_gemma":[0.002623364,0.0003751529,0.000745,0.0008011375,0.001020354,0.002827434,0.0009861554,0.002254688,0.0008038902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008903337,"about_ca_system_score_gemma":0.0008398876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001464904,"about_ca_topic_score_gemma":0.001890957,"domain_scores_codex":[0.9990288,0.0003096286,0.00007709881,0.0002149618,0.0003205232,0.00004905926],"domain_scores_gemma":[0.9994544,0.0002252782,0.00005893643,0.00004158343,0.0001989854,0.00002071388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003581277,0.00003335304,0.0002953162,0.0003601029,0.0000569859,0.0001886426,0.0001428087,0.3060315,0.004889573,0.5940139,0.005018281,0.08893383],"study_design_scores_gemma":[0.000009738882,0.00005632013,0.0001236346,0.00005171377,0.00001940931,0.0001274425,0.00001797017,0.8294362,0.001167331,0.1500419,0.01893165,0.00001670913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009350211,0.0005899343,0.9947551,0.000264295,0.0001364172,0.00002223891,0.00006684158,0.00004687781,0.003183242],"genre_scores_gemma":[0.1833461,0.003717271,0.7900551,0.0009209925,0.000992992,0.0005302309,0.0004542398,0.0001725902,0.01981057],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003755886,"threshold_uncertainty_score":0.01256472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0338229630465359,"score_gpt":0.247625159963031,"score_spread":0.2138021969164951,"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."}}