{"id":"W2967112359","doi":"10.1109/cec.2019.8790077","title":"Estimation of Distribution using Population Queue based Variational Autoencoders","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Virginia Agricultural Experiment Station, Virginia Polytechnic Institute and State University","keywords":"Autoencoder; Queue; Benchmark (surveying); Estimation of distribution algorithm; Computer science; Algorithm; Probabilistic logic; Population; Variance (accounting); Generative model; Mathematical optimization; Artificial intelligence; Mathematics; Artificial neural network; Generative grammar","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":[0.002257175,0.0007398556,0.0009542211,0.0008457449,0.0004715304,0.001165517,0.002326701,0.001450509,0.001473714],"category_scores_gemma":[0.006629543,0.0009113807,0.000941424,0.0006135424,0.0009327324,0.001741606,0.001389307,0.002056147,0.0003427254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241699,"about_ca_system_score_gemma":0.001765208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007383389,"about_ca_topic_score_gemma":0.008512149,"domain_scores_codex":[0.9993764,0.0002034937,0.00003439742,0.0001471645,0.000174262,0.0000643352],"domain_scores_gemma":[0.9970862,0.002027245,0.0001743517,0.0001820452,0.0004295693,0.0001005297],"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":[0.00003210171,0.00004376808,0.001139394,0.00003088649,0.00004337872,0.00003116262,0.00006353854,0.9295426,0.00178236,0.01242921,0.0005629607,0.05429859],"study_design_scores_gemma":[0.000002832347,0.00000420966,0.0000374283,0.000001453355,0.000001083506,0.000003951274,0.000001847215,0.998214,0.0001886417,0.001438881,0.0001035778,0.000002170869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006157367,0.0000681235,0.993169,0.00007498383,0.00001695638,0.00002086774,0.00001765937,0.0001712112,0.0003039508],"genre_scores_gemma":[0.3450123,0.0001762921,0.6512708,0.0002041836,0.0000661292,0.0002469962,0.0002489621,0.0002012601,0.002573014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007383389,"threshold_uncertainty_score":0.0146808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476844042489787,"score_gpt":0.2686683225407898,"score_spread":0.2538998821158919,"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."}}