{"id":"W2051717488","doi":"10.1162/neco_a_00014","title":"Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest","year":2010,"lang":"en","type":"article","venue":"Neural Computation","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Toronto","funders":"","keywords":"Restricted Boltzmann machine; Multivariate statistics; Boltzmann machine; Density estimation; Binary tree; Binary number; Boltzmann constant; Focus (optics); Mathematics; Tree (set theory); Extension (predicate logic); Computer science; Applied mathematics; Algorithm; Artificial intelligence; Statistics; Artificial neural network; Combinatorics; Physics","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":[0.003048589,0.0008015671,0.001590315,0.00086728,0.0004653605,0.001246599,0.002086407,0.001384911,0.002319417],"category_scores_gemma":[0.01471731,0.0007064383,0.00103169,0.001312147,0.001976247,0.003434915,0.002064295,0.002363615,0.0004637944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001338273,"about_ca_system_score_gemma":0.0008596121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004984554,"about_ca_topic_score_gemma":0.003301366,"domain_scores_codex":[0.9984747,0.0007706145,0.00005329536,0.0002823024,0.0003024315,0.0001166224],"domain_scores_gemma":[0.994136,0.004511539,0.0005054577,0.0005212716,0.0002180061,0.0001077471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008230723,0.00003334303,0.000717302,0.00007961103,0.00005607958,0.00008622753,0.0000769418,0.8089853,0.001050701,0.1567426,0.001129224,0.03096038],"study_design_scores_gemma":[0.000004936076,0.000004547648,0.0001033054,0.000006174135,0.000003883701,0.00002271085,0.000004286134,0.9299424,0.0002334034,0.06941267,0.0002551978,0.000006361799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007490593,0.000225707,0.9910209,0.000288654,0.00001091689,0.00001315343,0.00006392814,0.0001647384,0.000721484],"genre_scores_gemma":[0.6816122,0.0009030336,0.3127449,0.0003701456,0.0001795767,0.0001963186,0.0005614533,0.0002176236,0.003214723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004984554,"threshold_uncertainty_score":0.01612264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01153080014451868,"score_gpt":0.2401310256611353,"score_spread":0.2286002255166166,"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."}}