{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007847874,0.00009835161,0.0001212222,0.00001978533,0.00009657672,0.0002540272,0.0001187717,0.00002834139,0.000009650671],"category_scores_gemma":[0.0000379256,0.00008839434,0.00003382243,0.0002200943,0.00001383807,0.000618698,0.0001481077,0.0000455759,0.000005124353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000196321,"about_ca_system_score_gemma":0.00004595087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005179014,"about_ca_topic_score_gemma":0.00001356608,"domain_scores_codex":[0.9992489,0.00005078368,0.0001095422,0.0003177567,0.0001064318,0.0001665457],"domain_scores_gemma":[0.9994632,0.00007495899,0.00002831703,0.0002626369,0.00008854759,0.00008232175],"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":[0.000002944526,0.00003417998,0.0000274234,0.000006533637,0.00002153299,0.000008903579,0.0007637582,0.6551878,0.0238219,0.01023235,0.0003700875,0.3095226],"study_design_scores_gemma":[0.00007135535,0.00002078337,0.00001811785,0.00000869204,0.000007196425,0.000004829957,0.00002605395,0.9353567,0.05364823,0.01069427,0.00002818826,0.0001155582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002832548,0.00002391202,0.9956169,0.0003733279,0.0002628342,0.00008909526,0.000001281433,0.00003750053,0.0007626463],"genre_scores_gemma":[0.4683639,0.000002089628,0.5310069,0.0004479875,0.00004741678,0.000002252439,7.211603e-7,0.000004013212,0.0001247769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4655313,"threshold_uncertainty_score":0.3604617,"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."}}