{"id":"W3092253034","doi":"10.1109/tnnls.2020.3027761","title":"Clustering Analysis via Deep Generative Models With Mixture Models","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Huaqiao University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Autoencoder; Computer science; Artificial intelligence; Generative grammar; Mixture model; Generative model; Pattern recognition (psychology); Outlier; Correlation clustering; Machine learning; Deep learning; Data mining","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":[0.001194563,0.0009243667,0.0009352436,0.001206575,0.0005010035,0.001189248,0.001582225,0.001318888,0.002631687],"category_scores_gemma":[0.002914329,0.0008555121,0.001751832,0.001027118,0.001186832,0.001436115,0.00152858,0.001959326,0.0008856846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001377167,"about_ca_system_score_gemma":0.0008681336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005536449,"about_ca_topic_score_gemma":0.007869563,"domain_scores_codex":[0.9993599,0.0002107071,0.0000240028,0.0001741029,0.0001672054,0.00006404777],"domain_scores_gemma":[0.9988987,0.0006524831,0.0001226021,0.0001333114,0.0001381246,0.00005464864],"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":[0.00003037685,0.00001513532,0.0006206927,0.00003224558,0.00004029417,0.00004316771,0.00006886941,0.9360873,0.001449092,0.0420602,0.001121293,0.01843131],"study_design_scores_gemma":[0.00000200535,0.000003385576,0.00005347536,0.000003133843,0.000003263378,0.00001131934,0.000003696453,0.9880159,0.0002186928,0.01135455,0.0003263418,0.000004111933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004826922,0.0001371466,0.9935496,0.0001135769,0.00001293068,0.00001683957,0.00008050875,0.0003106399,0.0009517993],"genre_scores_gemma":[0.5735297,0.0008759634,0.4116755,0.0004066718,0.00009567111,0.0002918515,0.001230326,0.0007520546,0.01114233],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005536449,"threshold_uncertainty_score":0.01100844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880910722640645,"score_gpt":0.2044349982190899,"score_spread":0.1856258909926834,"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."}}