{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001799375,0.0003298867,0.0004877096,0.0001288737,0.0006832286,0.0004638623,0.000296284,0.0001220471,0.000005962739],"category_scores_gemma":[0.000001684896,0.0002586197,0.0001644869,0.001036211,0.00005246349,0.0008220976,0.000008007775,0.000684468,0.000001730526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002513452,"about_ca_system_score_gemma":0.00001239366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001201545,"about_ca_topic_score_gemma":0.00007157234,"domain_scores_codex":[0.9978346,0.0004788715,0.000313307,0.0007083628,0.0002773337,0.0003875395],"domain_scores_gemma":[0.9990724,0.0001496294,0.0001494784,0.0002657046,0.0001051947,0.0002575658],"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":[0.00004690835,0.00001713722,0.000008706476,0.00001161554,0.0003614034,0.00001511627,0.001060986,0.9853567,0.0000781823,0.00006003256,0.00001622445,0.01296694],"study_design_scores_gemma":[0.0002912281,0.0003074964,0.000007503827,0.00002266112,0.0001971163,0.00001752719,0.0001411519,0.9985989,0.00004735394,0.00001137621,0.00004317365,0.0003144827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001159738,0.0005520273,0.9968942,0.0005174236,0.0003130994,0.0002627257,0.000002129347,0.0001957183,0.0001029014],"genre_scores_gemma":[0.9897603,0.00008424311,0.009295734,0.0004165792,0.0002499275,0.00003986491,0.000002888715,0.00002729158,0.0001231999],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9886006,"threshold_uncertainty_score":0.9999866,"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."}}