{"id":"W2409550820","doi":"10.48550/arxiv.1605.08803","title":"Density Estimation Using Real NVP","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":793,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Latent variable; Inference; Computer science; Artificial intelligence; Unsupervised learning; Sampling (signal processing); Machine learning; Probabilistic logic; Computation; Set (abstract data type); Bayesian inference; Latent variable model; Bayesian probability; Algorithm","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":[0.00324183,0.0007316808,0.0009300187,0.0009850271,0.0005674392,0.00183571,0.002090792,0.001251451,0.002593561],"category_scores_gemma":[0.01928101,0.0005966327,0.0009823153,0.0008494318,0.002169169,0.003409432,0.002927315,0.003095252,0.0006882127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001672274,"about_ca_system_score_gemma":0.001434768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003410663,"about_ca_topic_score_gemma":0.003283798,"domain_scores_codex":[0.998058,0.001122775,0.00007175371,0.0003160184,0.0003575686,0.0000738764],"domain_scores_gemma":[0.9931881,0.004572396,0.0004474826,0.001114867,0.0005285484,0.0001485768],"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":[0.00008753867,0.00005554212,0.001345303,0.0001109185,0.00004572052,0.0001301196,0.000221908,0.662335,0.002177524,0.2529472,0.002706579,0.07783659],"study_design_scores_gemma":[0.00000423735,0.000006496988,0.00005224823,0.000005319919,0.00000121826,0.00002125704,0.000008128132,0.9288274,0.0004303533,0.07004339,0.0005949837,0.000004969006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004676104,0.00005611271,0.9939955,0.0001527488,0.00001234416,0.00002372602,0.00005527908,0.0003149063,0.0007132619],"genre_scores_gemma":[0.3869121,0.0002575354,0.607407,0.0002463905,0.00008972988,0.0003498567,0.0007435858,0.0005528955,0.003440966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003410663,"threshold_uncertainty_score":0.01714468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09837519305503026,"score_gpt":0.2189231448305726,"score_spread":0.1205479517755423,"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."}}