{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002810333,0.0001966183,0.0001910303,0.0001853678,0.0001890768,0.0001263643,0.001094609,0.0002111458,0.00001243958],"category_scores_gemma":[0.0000624854,0.0001993636,0.00009320924,0.0002582321,0.00005982301,0.0004726624,0.001270725,0.0003386837,0.000127021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000238562,"about_ca_system_score_gemma":0.0001760746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000392531,"about_ca_topic_score_gemma":0.00002317062,"domain_scores_codex":[0.9985068,0.0001780164,0.0001467357,0.0008640297,0.00008696132,0.0002174452],"domain_scores_gemma":[0.9981106,0.00007764868,0.0002880587,0.001294873,0.0001201629,0.0001086359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002951172,0.00008308622,0.01784689,0.00009916179,0.00006412606,0.0001406341,0.0002145513,0.1929496,0.000573848,0.7729112,0.0004046129,0.01468282],"study_design_scores_gemma":[0.0001692697,0.00001177417,0.00906796,0.00006751225,0.00002867969,0.000004728742,0.000005424843,0.9454781,0.00008122806,0.04466517,0.0001756673,0.0002445109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2013023,0.00000604287,0.7952811,0.0001457329,0.0003364702,0.00009903911,0.00000795521,0.0002685189,0.002552793],"genre_scores_gemma":[0.9870773,0.00004869158,0.01204558,0.00002852784,0.00007909149,2.187277e-7,0.0000366932,0.00001007312,0.0006738522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7857749,"threshold_uncertainty_score":0.8129809,"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."}}