{"id":"W1586024463","doi":"10.1109/icosp.2002.1181050","title":"Decorrelated algorithms for faster adaptation","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Decorrelation; Autocorrelation matrix; Adaptive filter; Autocorrelation; Algorithm; Computer science; Filter (signal processing); Noise (video); Kernel adaptive filter; Computational complexity theory; SIGNAL (programming language); Noise power; Covariance matrix; Adaptation (eye); Filter design; Control theory (sociology); Power (physics); Mathematics; Artificial intelligence; Statistics","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":[0.00004093283,0.00007666609,0.00006290503,0.00003622123,0.0000182447,0.000008311247,0.00003721407,0.00004114218,0.00005873222],"category_scores_gemma":[0.0000230724,0.00007535376,0.0000247697,0.00005369332,0.000006496732,0.000105813,0.000003501337,0.00004217329,0.00001965519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003215597,"about_ca_system_score_gemma":0.000002946275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.916788e-7,"about_ca_topic_score_gemma":0.000002272212,"domain_scores_codex":[0.9996607,0.000004405774,0.00009302842,0.00008129748,0.000035581,0.0001250069],"domain_scores_gemma":[0.9998225,0.00002920415,0.000008500251,0.00008798492,0.00002716752,0.00002465845],"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":[0.00003178986,0.00007813019,0.0001945384,0.0001639601,0.000181123,0.000008971102,0.001030203,0.403702,0.07238368,0.1540083,0.01827503,0.3499423],"study_design_scores_gemma":[0.0004011831,0.00007641687,0.00009422637,0.00002041973,0.000009097985,0.000007964183,0.0000953647,0.7286102,0.136345,0.01248097,0.1215262,0.0003329872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001839646,0.0000409853,0.9861701,0.000004240824,0.0001672461,0.0001805688,0.000003511018,0.0009785927,0.01061511],"genre_scores_gemma":[0.3453671,0.000009481134,0.6536539,0.00002138158,0.00001545428,0.0000712309,0.000005987477,0.00003108475,0.0008243749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3496093,"threshold_uncertainty_score":0.3072837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266356864032033,"score_gpt":0.2563996255632288,"score_spread":0.2237360569229084,"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."}}