{"id":"W2927683843","doi":"10.5430/air.v8n1p41","title":"A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Deep learning; Artificial neural network; Feature (linguistics); Stage (stratigraphy); Algorithm; Multilayer perceptron; Backpropagation; Perceptron; Feature extraction; Encoder; Convolutional neural network; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001338988,0.0001802141,0.0001831258,0.000245927,0.000514605,0.000316456,0.0006482842,0.00009646809,0.000137717],"category_scores_gemma":[0.00009150099,0.0001444408,0.00007831751,0.0008912906,0.0001458471,0.0003543012,0.0001188462,0.0006024828,0.0005480947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001227805,"about_ca_system_score_gemma":0.0001123792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001601455,"about_ca_topic_score_gemma":0.0001966553,"domain_scores_codex":[0.9974127,0.0002368475,0.0002554914,0.0007346879,0.0007128109,0.0006474525],"domain_scores_gemma":[0.9972715,0.00149302,0.00007375805,0.0005578732,0.0004659705,0.0001379255],"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":[0.0001060749,0.0001976376,0.0002902218,0.00001724382,0.00001157856,0.000006142263,0.0005770019,0.07057109,0.002642594,0.03161562,0.00006146447,0.8939033],"study_design_scores_gemma":[0.00008723784,0.0009590725,0.0002556712,0.00004413514,0.000003159805,0.000001010785,0.0001987042,0.9737756,0.01779055,0.005056052,0.001641228,0.0001876313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0198647,0.00001361983,0.9765856,0.0007976294,0.0001314635,0.001964038,0.000004733124,0.000113495,0.0005247471],"genre_scores_gemma":[0.9491713,0.00000627439,0.04958663,0.0001148296,0.0002117292,0.0006696769,0.00001466954,0.00002665729,0.0001982325],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9293066,"threshold_uncertainty_score":0.7044832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028277012464106,"score_gpt":0.3815450569620924,"score_spread":0.2787173557156818,"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."}}