{"id":"W2111838090","doi":"10.1162/neco.2010.08-09-1081","title":"Deep Belief Networks Are Compact Universal Approximators","year":2010,"lang":"en","type":"article","venue":"Neural Computation","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Deep belief network; Artificial intelligence; Sigmoid function; Generative grammar; Deep neural networks; Deep learning; Computer science; Artificial neural network; Generative model; Expression (computer science); Mathematics; Theoretical computer science","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":[0.001085132,0.001075084,0.0009224786,0.0007917586,0.000321009,0.00165819,0.001482948,0.001464263,0.003260913],"category_scores_gemma":[0.006829606,0.0007660887,0.0007843979,0.0007904371,0.002086686,0.003844328,0.001913349,0.003711831,0.0009663077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048165,"about_ca_system_score_gemma":0.0005909055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001529823,"about_ca_topic_score_gemma":0.001701635,"domain_scores_codex":[0.9992722,0.0002040082,0.00004309118,0.0001832517,0.0002294295,0.00006801192],"domain_scores_gemma":[0.9982242,0.001139179,0.0001745301,0.0002287314,0.0001859724,0.0000473706],"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":[0.00006811333,0.00002408441,0.0004777575,0.0002212927,0.00006315255,0.0000857984,0.0001396151,0.301913,0.002317523,0.621956,0.003047211,0.06968642],"study_design_scores_gemma":[0.00001029691,0.00001916787,0.00009003626,0.00005033348,0.00001415241,0.00003646203,0.00001404252,0.6212382,0.001123688,0.3737317,0.003661253,0.00001058609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003851811,0.0007196885,0.9917548,0.0003672261,0.00003413295,0.00001726667,0.0001464897,0.0002976236,0.002810972],"genre_scores_gemma":[0.5976596,0.004070986,0.3850328,0.0006164446,0.0002342044,0.0002783132,0.0007775998,0.0003203359,0.01100966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003260913,"threshold_uncertainty_score":0.01090884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013630683391467,"score_gpt":0.2242362151020559,"score_spread":0.2140999082681412,"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."}}