{"id":"W3111363863","doi":"10.1109/smc42975.2020.9283007","title":"Variational Inference of Infinite Generalized Gaussian Mixture Models with Feature Selection","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Mixture model; Cluster analysis; Inference; Gaussian; Model selection; Feature selection; Selection (genetic algorithm); Computer science; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Gaussian process; Generalized normal distribution; Flexibility (engineering); Algorithm; Mathematics; Mathematical optimization; Normal distribution; Statistics","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":[0.0001353372,0.0001523692,0.0002124808,0.00005986133,0.00005900839,0.00007031237,0.00039813,0.0001137375,0.00003718744],"category_scores_gemma":[0.00002349455,0.0001072276,0.0000497242,0.0006712386,0.00002349866,0.0005431532,0.00008104091,0.0001983773,0.000002975735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001194186,"about_ca_system_score_gemma":0.0001656422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002374409,"about_ca_topic_score_gemma":0.00001096876,"domain_scores_codex":[0.9989063,0.0001068044,0.000169878,0.0003521619,0.0002899804,0.0001748797],"domain_scores_gemma":[0.9993104,0.00005458909,0.0001128831,0.0002117994,0.000174859,0.0001354629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003037777,0.00002220493,0.000153277,0.00001817308,0.0000288137,0.000002075567,0.0008185967,0.004576795,0.002754428,0.9839133,0.001105695,0.006576277],"study_design_scores_gemma":[0.0005228217,0.0001705254,0.0004861864,0.00001476257,0.00001253889,0.00001152472,0.0000033952,0.9134287,0.003669978,0.08076365,0.000718693,0.0001971767],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004051599,0.00004100379,0.9862128,0.006107984,0.00004450156,0.000131781,0.000004017194,0.0001188361,0.006933879],"genre_scores_gemma":[0.2391493,0.000007700875,0.7587368,0.001777875,0.00006560804,0.000006813217,0.000004351088,0.000006780037,0.0002447414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.908852,"threshold_uncertainty_score":0.4372616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611439777089712,"score_gpt":0.25834608272426,"score_spread":0.2322316849533629,"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."}}