{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002041206,0.001232234,0.001139601,0.001308357,0.000449669,0.001707469,0.002629929,0.001624188,0.007165356],"category_scores_gemma":[0.006728185,0.0008406202,0.001732113,0.001229406,0.001776296,0.002676194,0.003279457,0.003731431,0.002679916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001529842,"about_ca_system_score_gemma":0.00149709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003268925,"about_ca_topic_score_gemma":0.003793935,"domain_scores_codex":[0.9990528,0.0002934744,0.00004231239,0.0002670535,0.0002549752,0.00008933391],"domain_scores_gemma":[0.998264,0.0009841293,0.0001442221,0.0003463534,0.0001737946,0.00008749914],"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":[0.0001185332,0.00007050968,0.001051697,0.0001726898,0.0001133154,0.0001473092,0.0001579525,0.5824618,0.003694813,0.2787485,0.01355684,0.1197061],"study_design_scores_gemma":[0.000008704636,0.0000117825,0.00009516278,0.00001528199,0.00001048425,0.00004464458,0.000004122761,0.9218139,0.0007224822,0.07442992,0.002830325,0.00001317184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0012864,0.0001083162,0.9961649,0.00019509,0.00003698242,0.0000340701,0.000284764,0.0006530195,0.001236426],"genre_scores_gemma":[0.339069,0.001344945,0.6304897,0.001419249,0.0003400325,0.001018488,0.004876506,0.001816237,0.01962589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007165356,"threshold_uncertainty_score":0.02397048,"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."}}