{"id":"W2948700496","doi":"10.48550/arxiv.1906.02735","title":"Residual Flows for Invertible Generative Modeling","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Invertible matrix; Residual; Lipschitz continuity; Estimator; Discriminative model; Transformation (genetics); Density estimation; Mathematics; Computer science; Applied mathematics; Artificial neural network; Algorithm; Mathematical optimization; Artificial intelligence; Statistics; Pure mathematics","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":[0.001109184,0.0009570947,0.0005082515,0.0006773246,0.0003276083,0.0008418289,0.001127353,0.001017146,0.005813816],"category_scores_gemma":[0.004716793,0.0004875663,0.0007781509,0.0004746054,0.001366828,0.00164206,0.001676068,0.002196806,0.001313774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007992846,"about_ca_system_score_gemma":0.0007174945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002148842,"about_ca_topic_score_gemma":0.002773931,"domain_scores_codex":[0.9995673,0.0001636976,0.00001987443,0.0001123781,0.0001009541,0.00003576539],"domain_scores_gemma":[0.9987944,0.0008092853,0.0000913193,0.0001858562,0.00008488402,0.00003418881],"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":[0.00005986192,0.00003576455,0.0003658548,0.00009272551,0.00003183593,0.00007976891,0.0001257658,0.66515,0.005668146,0.2356916,0.002003053,0.09069555],"study_design_scores_gemma":[0.00000352249,0.00001156567,0.00003414982,0.00001019937,0.000004764461,0.00002265188,0.000004586195,0.9371874,0.001289217,0.05971251,0.001714204,0.000005251244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003315211,0.0001098778,0.99394,0.0001055146,0.00001519861,0.00001887104,0.00006214657,0.000365093,0.002068081],"genre_scores_gemma":[0.4936858,0.00077112,0.4896292,0.0004281143,0.0001277818,0.0003292719,0.0007242223,0.0007046117,0.01359993],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005813816,"threshold_uncertainty_score":0.01944911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1108893237547569,"score_gpt":0.1968654022686914,"score_spread":0.08597607851393453,"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."}}