{"id":"W2809769628","doi":"10.1145/3197517.3201359","title":"Blended cured quasi-newton for distortion optimization","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Broyden–Fletcher–Goldfarb–Shanno algorithm; Distortion (music); Line search; Computer science; Gradient descent; Laplace operator; Mathematical optimization; Convergence (economics); Norm (philosophy); Algorithm; Geometry processing; Polygon mesh; Mathematics; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006295306,0.0001429186,0.0001457663,0.0002196213,0.0001741258,0.00001544133,0.0001555154,0.0001090991,0.00005077767],"category_scores_gemma":[0.00002722249,0.0001481707,0.0001387349,0.0005769532,0.00007242149,0.00017638,0.000001402802,0.0001352441,0.000007228206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006287655,"about_ca_system_score_gemma":0.000006894638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000665132,"about_ca_topic_score_gemma":0.00005477663,"domain_scores_codex":[0.9992859,0.00001326977,0.0002075516,0.0001877961,0.0001275277,0.000177893],"domain_scores_gemma":[0.9993494,0.00006962023,0.00003314423,0.0003847409,0.0001055484,0.00005752341],"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":[0.0002003781,0.0006340969,0.00005553038,0.0001403409,0.000418822,0.000001174082,0.0003510167,0.8293451,0.006357417,0.003140233,0.002596806,0.1567591],"study_design_scores_gemma":[0.0006089494,0.0007246023,0.00009537338,0.00004431267,0.0002396599,0.000002993659,0.00004197205,0.860674,0.09099501,0.02805316,0.01793275,0.0005872228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000726283,0.00002913962,0.9974578,0.0003353073,0.0001934068,0.0002214629,0.00002500482,0.0008945774,0.0001170138],"genre_scores_gemma":[0.853695,0.0002921734,0.1454945,0.0001018897,0.00007348256,0.0001666786,0.0000412179,0.00005067145,0.00008437328],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8529687,"threshold_uncertainty_score":0.6042227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475546513468351,"score_gpt":0.2647259891890033,"score_spread":0.2499705240543198,"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."}}