{"id":"W2920877073","doi":"10.1109/taslp.2019.2903276","title":"Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems","year":2019,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Algorithm; Adaptive filter; Computer science; Recursive least squares filter; Robustness (evolution); System identification; Rate of convergence; Finite impulse response; Mathematics; Mathematical optimization; Key (lock)","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":[0.001393003,0.001666789,0.001344436,0.0008856707,0.0004840181,0.001050644,0.001196889,0.001316083,0.003777855],"category_scores_gemma":[0.005191126,0.0006452875,0.001043669,0.001461166,0.0008366816,0.001131907,0.001172286,0.002815699,0.003072555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006291664,"about_ca_system_score_gemma":0.001148061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002963673,"about_ca_topic_score_gemma":0.003199673,"domain_scores_codex":[0.9988985,0.0003842389,0.00007454268,0.0001982345,0.000388611,0.00005573849],"domain_scores_gemma":[0.9981932,0.001064023,0.0001830761,0.0001971199,0.0003303603,0.00003215549],"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":[0.00008469239,0.00007178605,0.0005840862,0.0005224068,0.0001429976,0.0001156749,0.0002074597,0.5514079,0.009724813,0.08735429,0.005086242,0.3446977],"study_design_scores_gemma":[0.00001361562,0.00002783609,0.0001738229,0.00002312531,0.00001239249,0.00004549666,0.00001427697,0.9721695,0.001581811,0.02038782,0.005528419,0.0000219852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005417305,0.000507539,0.9981788,0.00005670423,0.00002408186,0.0000161469,0.00002158037,0.0002218636,0.0004316347],"genre_scores_gemma":[0.05353116,0.001963702,0.9393436,0.0001096904,0.000146094,0.0003042913,0.00032481,0.0002094545,0.004067179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003777855,"threshold_uncertainty_score":0.01263821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310213587276607,"score_gpt":0.2644828462896952,"score_spread":0.2513807104169291,"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."}}