{"id":"W3160487084","doi":"10.32473/flairs.v34i1.128379","title":"Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Agence Nationale de la Recherche; Equipex","keywords":"Cluster analysis; Inference; Artificial intelligence; Entropy (arrow of time); Mixture model; Unsupervised learning; Computer science; Categorization; Pattern recognition (psychology); BETA (programming language); Kullback–Leibler divergence; Machine learning; Algorithm; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00319603,0.0006921837,0.00139527,0.001179119,0.0006120437,0.001424715,0.00242223,0.001360679,0.001864923],"category_scores_gemma":[0.007968672,0.0008164762,0.001236046,0.0008681567,0.001781846,0.002411529,0.0017774,0.002023411,0.0004548467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502574,"about_ca_system_score_gemma":0.001265561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006006456,"about_ca_topic_score_gemma":0.005149459,"domain_scores_codex":[0.9990305,0.0004802334,0.00003757456,0.0001638277,0.0002080624,0.0000798004],"domain_scores_gemma":[0.9975181,0.001824031,0.0001410588,0.0001224869,0.0002832273,0.000111142],"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":[0.00006139933,0.00003290929,0.001016144,0.00006436528,0.00006010872,0.00006309777,0.000123047,0.8356134,0.001872286,0.1327383,0.0009812484,0.0273737],"study_design_scores_gemma":[0.000001890209,0.00000384163,0.00004408205,0.000002545668,0.000002129654,0.00000555912,0.000002470023,0.9876038,0.0001030623,0.01210178,0.0001244027,0.000004403355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005151858,0.0001246449,0.993969,0.00009398617,0.00001183595,0.00001024081,0.00002099334,0.00005889604,0.000558562],"genre_scores_gemma":[0.6271057,0.0008094687,0.363131,0.0003286738,0.0001399636,0.0002667023,0.0005711655,0.0003189759,0.007328517],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006006456,"threshold_uncertainty_score":0.01690245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.113545119647876,"score_gpt":0.3514613912485156,"score_spread":0.2379162716006397,"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."}}