{"id":"W1783453637","doi":"10.1007/978-3-642-55032-4_28","title":"Online Learning for Two Novel Latent Topic Models","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Latent Dirichlet allocation; Topic model; Computer science; Dirichlet distribution; Artificial intelligence; Machine learning; Latent variable; Mathematics","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":[0.006257395,0.001445787,0.002647251,0.001580546,0.001298001,0.003353112,0.005128033,0.004335623,0.009906343],"category_scores_gemma":[0.02525013,0.001596585,0.002603419,0.002303551,0.001491321,0.008661238,0.004785108,0.005889405,0.002621012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001892696,"about_ca_system_score_gemma":0.001472117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002849139,"about_ca_topic_score_gemma":0.004395791,"domain_scores_codex":[0.9973241,0.001017859,0.0001756162,0.0007455089,0.0004530654,0.0002838531],"domain_scores_gemma":[0.9791257,0.017731,0.0004727135,0.001466921,0.0007335102,0.0004701268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001484553,0.0006485804,0.002680254,0.0008080748,0.0003314922,0.0003422625,0.0009530491,0.1866765,0.00433205,0.2496499,0.02336062,0.5287326],"study_design_scores_gemma":[0.00007449069,0.00002830477,0.0001743806,0.00002341338,0.0000419244,0.0000646133,0.00003055798,0.9064615,0.0004755072,0.09124799,0.001356493,0.00002073462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01149846,0.0004240786,0.98565,0.0006051949,0.0001007804,0.00005708247,0.000189081,0.0005172531,0.0009581178],"genre_scores_gemma":[0.2782284,0.0009118259,0.7024371,0.0005337729,0.0008037033,0.0008265128,0.002475564,0.0006067941,0.0131763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009906343,"threshold_uncertainty_score":0.03314006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0398527244333944,"score_gpt":0.2890081609870012,"score_spread":0.2491554365536068,"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."}}