{"id":"W2899905351","doi":"10.23919/ecc.2019.8796305","title":"Adaptive Hessian Estimation Based Extremum Localization","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Extremum Seeking Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Robustness (evolution); Quadratic equation; Convergence (economics); Mathematics; Stability (learning theory); SIGNAL (programming language); Function (biology); Control theory (sociology); Rate of convergence; Noise (video); Applied mathematics; Computer science; Geometry; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004411661,0.0004777923,0.0005315487,0.0002873671,0.000203451,0.000450634,0.0006169001,0.0005450792,0.0008078669],"category_scores_gemma":[0.001255142,0.0002031564,0.0002918919,0.0002706293,0.0006096712,0.0005999769,0.0006275133,0.0004578304,0.0001891075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003676355,"about_ca_system_score_gemma":0.000271532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00117653,"about_ca_topic_score_gemma":0.000677673,"domain_scores_codex":[0.9997889,0.00006018275,0.000008361633,0.0000580708,0.00006239543,0.00002199808],"domain_scores_gemma":[0.9996861,0.000151869,0.00005901056,0.00002738353,0.00006367068,0.00001206868],"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":[0.0002135452,0.00004556302,0.001088286,0.0002736625,0.00008026019,0.000267873,0.0002740866,0.7463202,0.07254416,0.04373999,0.001150897,0.1340014],"study_design_scores_gemma":[0.000004723874,0.00005371191,0.0002168063,0.000004989548,0.000006096299,0.00005251664,0.000009430424,0.9930407,0.003914786,0.002191812,0.0004948772,0.000009568095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01452741,0.0001778499,0.9842508,0.00004156814,0.00001785533,0.000009831525,0.00000825814,0.00008497071,0.0008815259],"genre_scores_gemma":[0.8627708,0.00029834,0.1334971,0.00004625382,0.00004135882,0.00004135886,0.00003438829,0.00004893318,0.003221601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00117653,"threshold_uncertainty_score":0.002702653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450150161759563,"score_gpt":0.2143128833776964,"score_spread":0.1998113817601008,"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."}}