{"id":"W2913117037","doi":"10.48550/arxiv.1902.01893","title":"TzK: Flow-Based Conditional Generative Model","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Prior probability; Joint probability distribution; Computer science; Class (philosophy); Generative model; Conditional probability distribution; Generative grammar; A priori and a posteriori; Machine learning; Artificial intelligence; Conditional probability; Mathematics; Statistics; Bayesian probability","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":[0.0001987123,0.0004169235,0.0004173761,0.0002121833,0.0002204267,0.0001935646,0.001557021,0.0003093814,0.00009077153],"category_scores_gemma":[0.0000264357,0.0004625413,0.0003368273,0.0003339468,0.0001307631,0.0004661287,0.001159211,0.0005201244,0.0002161526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522843,"about_ca_system_score_gemma":0.0006856734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003396427,"about_ca_topic_score_gemma":0.00001852551,"domain_scores_codex":[0.9976829,0.0002182967,0.0002110258,0.001338279,0.0001505603,0.0003989607],"domain_scores_gemma":[0.9979514,0.0001340259,0.0002388378,0.001189801,0.000313619,0.0001723471],"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":[0.00001674238,0.00006524384,0.000092727,0.00001784324,0.00008385344,0.00005624048,0.00005700232,0.950174,0.00008032451,0.04650562,0.00269191,0.0001585101],"study_design_scores_gemma":[0.0004940273,0.00003372524,0.0000936902,0.00003661112,0.00005006461,8.359261e-7,0.00001056622,0.9580311,0.0008939653,0.03957738,0.000276562,0.0005014272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005301036,0.00004841765,0.9901223,0.0002762696,0.0007109047,0.0003367183,0.0001342222,0.0001571565,0.002913011],"genre_scores_gemma":[0.939368,0.00003003571,0.05733654,0.000605589,0.0001788379,0.000002275487,0.0001371222,0.00002136801,0.002320211],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.934067,"threshold_uncertainty_score":0.9997826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0722996957571259,"score_gpt":0.1822728488485108,"score_spread":0.1099731530913849,"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."}}