{"id":"W3131495249","doi":"","title":"Using MMD GANs to correct physics models and improve Bayesian parameter estimation","year":2021,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Fiducial inference; Bayesian inference; Computer science; Artificial intelligence; Bayes' theorem; Machine learning; Algorithm; Bayesian probability; Uncertainty quantification; Estimation theory; Frequentist inference","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":[0.002110494,0.001263623,0.0009547636,0.0007921359,0.000343848,0.0008687766,0.00141654,0.001259161,0.001975402],"category_scores_gemma":[0.007454237,0.0009575047,0.0008824811,0.000508296,0.001107097,0.001559799,0.001739176,0.002834106,0.0006366577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082693,"about_ca_system_score_gemma":0.000993157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004759557,"about_ca_topic_score_gemma":0.007037647,"domain_scores_codex":[0.9992905,0.0002836826,0.00003179552,0.0001728635,0.0001757432,0.00004543309],"domain_scores_gemma":[0.9972084,0.001949653,0.0002137815,0.0003177031,0.0002358676,0.00007466671],"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":[0.00004484985,0.00002897594,0.000807944,0.0000545025,0.00005314575,0.00005835951,0.00005950668,0.93388,0.003403802,0.02384475,0.001600776,0.03616335],"study_design_scores_gemma":[0.000002466669,0.000004122589,0.00005304533,0.000004360855,0.000003023391,0.00001077903,0.000001844379,0.9922971,0.0004672958,0.006820263,0.0003319953,0.000003687663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004941997,0.0001308438,0.9932882,0.0001743202,0.00002335665,0.00001615062,0.00005229819,0.0004488722,0.0009239203],"genre_scores_gemma":[0.4510708,0.0005152872,0.5407949,0.0008857183,0.0001248969,0.0001895298,0.0007784353,0.0006683346,0.00497212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004759557,"threshold_uncertainty_score":0.01116151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120423736067441,"score_gpt":0.2713950467791765,"score_spread":0.2301908094185021,"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."}}