{"id":"W2786294823","doi":"10.1109/ssci.2017.8280810","title":"On learning the structure of sum-product networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Mutual information; Cluster analysis; Mixture model; Machine learning; Gaussian; Heuristic; Pattern recognition (psychology); Product (mathematics); Artificial neural network; Mathematics","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":[0.004668179,0.001911052,0.001685579,0.002267489,0.001089507,0.001901478,0.002842976,0.002460098,0.002769215],"category_scores_gemma":[0.03326515,0.001467677,0.001472925,0.002044383,0.00331114,0.007415951,0.003297935,0.005007195,0.0008112785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002537352,"about_ca_system_score_gemma":0.001612073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006405769,"about_ca_topic_score_gemma":0.009175265,"domain_scores_codex":[0.9976768,0.000968791,0.0001139856,0.0007259196,0.0003850654,0.0001294627],"domain_scores_gemma":[0.9838321,0.01213454,0.001032885,0.001409562,0.001238781,0.0003521261],"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":[0.0001593602,0.0001049751,0.00430059,0.0001908225,0.0001613023,0.00009659087,0.0002258227,0.7959046,0.00114014,0.06659594,0.003375699,0.1277441],"study_design_scores_gemma":[0.000006785391,0.00002427732,0.0001980166,0.00002423908,0.000009226529,0.00002312995,0.00001088983,0.9313829,0.0003282181,0.06765007,0.0003340626,0.000008220956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02836069,0.0006734728,0.9676839,0.0007149148,0.00003204354,0.00005600695,0.0002860556,0.0007313071,0.00146154],"genre_scores_gemma":[0.5917056,0.001645928,0.3961999,0.001047687,0.0002151929,0.0004051227,0.003136692,0.0005432113,0.005100611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006405769,"threshold_uncertainty_score":0.02468795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02318692922139953,"score_gpt":0.2644788777837858,"score_spread":0.2412919485623862,"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."}}