{"id":"W2033272835","doi":"10.1162/neco.2008.12-07-661","title":"Deep, Narrow Sigmoid Belief Networks Are Universal Approximators","year":2008,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sigmoid function; Curse of dimensionality; Binary number; Computer science; Deep neural networks; Artificial intelligence; Deep belief network; Artificial neural network; Algorithm; Theoretical computer science; Mathematics; Arithmetic","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005863272,0.0001646024,0.0001535759,0.00007435384,0.0004498122,0.00008314235,0.0004808825,0.00006831111,0.000005056878],"category_scores_gemma":[0.000006280376,0.0001612908,0.00007652169,0.0006359732,0.00006432161,0.0005004774,0.0001289423,0.0002062583,0.00002989703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003775849,"about_ca_system_score_gemma":0.00001946362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000112733,"about_ca_topic_score_gemma":0.000005898153,"domain_scores_codex":[0.9987741,0.00006786259,0.0002166016,0.0004134565,0.0002388796,0.0002891104],"domain_scores_gemma":[0.99925,0.00009406443,0.0001606257,0.0002766689,0.00009645937,0.0001221768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006779884,0.0000702871,0.00151221,0.000006146747,0.000007325112,0.00005365229,0.0002692151,0.9631807,0.00009474131,0.009096294,0.005350335,0.02035227],"study_design_scores_gemma":[0.0002331355,0.00005082368,0.008023147,0.000005221138,0.000003674135,0.00007907592,0.00001525566,0.98985,0.000058139,0.0008129553,0.0006892677,0.0001792914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.135848,0.0001523831,0.8615233,0.00110469,0.0003437973,0.0002240325,6.167184e-7,0.0003864119,0.0004167592],"genre_scores_gemma":[0.9889008,0.00001476203,0.01014853,0.0006113776,0.0001904175,0.00001432967,0.00001673001,0.0000134874,0.00008960946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8530528,"threshold_uncertainty_score":0.6577249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01677067633082922,"score_gpt":0.2210510176949123,"score_spread":0.204280341364083,"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."}}