{"id":"W2970641149","doi":"","title":"Residual Flows for Invertible Generative Modeling","year":2019,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Invertible matrix; Residual; Lipschitz continuity; Discriminative model; Transformation (genetics); Computer science; Mathematics; Algorithm; Generative model; Artificial neural network; Density estimation; Applied mathematics; Mathematical optimization; Generative grammar; Artificial intelligence; Statistics; Estimator; 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":[0.001016921,0.0009239677,0.0005027968,0.0006278319,0.0003183608,0.0007984932,0.001080502,0.0009322083,0.00625964],"category_scores_gemma":[0.004220012,0.000481652,0.0007726405,0.0004450699,0.001292006,0.00155851,0.001646409,0.001969204,0.001273288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006978678,"about_ca_system_score_gemma":0.0007615968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002121809,"about_ca_topic_score_gemma":0.002756505,"domain_scores_codex":[0.9996305,0.0001223086,0.00001840667,0.00009800078,0.00009621806,0.00003468528],"domain_scores_gemma":[0.998973,0.0006699061,0.00008404544,0.0001601525,0.0000809496,0.00003194721],"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":[0.00005913954,0.00003487523,0.000351858,0.00009556126,0.00003230945,0.00008438152,0.0001251444,0.6761876,0.007024287,0.2206327,0.001857278,0.09351496],"study_design_scores_gemma":[0.000003613492,0.00001280068,0.00003524086,0.00000994842,0.000005022715,0.00002569501,0.000004863412,0.9475002,0.001407042,0.04917727,0.001812619,0.000005621399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003127699,0.0001017034,0.9941794,0.00008803472,0.00001529073,0.00001913183,0.00005795546,0.0003457121,0.002065124],"genre_scores_gemma":[0.4842017,0.0007252274,0.5004604,0.0003917788,0.0001152497,0.0003247683,0.0006510193,0.000645058,0.0124848],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00625964,"threshold_uncertainty_score":0.0209406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02398896971344965,"score_gpt":0.2394272595567821,"score_spread":0.2154382898433325,"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."}}