{"id":"W4404787947","doi":"10.1109/tcsi.2024.3502519","title":"Attention Mechanism Combined With Deep Recurrent Network for Nonlinear Circuit Macromodeling","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Nonlinear system; Equivalent circuit; Mechanism (biology); Computer science; Network analysis; Electronic engineering; Control theory (sociology); Voltage; Engineering; Electrical engineering; Physics; Artificial intelligence","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":[0.0002949957,0.000231254,0.0002501608,0.00007821951,0.0004993421,0.0004597056,0.0002487817,0.0001019002,0.000003494611],"category_scores_gemma":[0.000001034861,0.0001880814,0.0001321653,0.0004217382,0.00003649461,0.0002200541,0.000002274035,0.0001950589,0.00000780042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005321905,"about_ca_system_score_gemma":0.00005138856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008937408,"about_ca_topic_score_gemma":0.00002379904,"domain_scores_codex":[0.9983675,0.00005661808,0.0003154788,0.0006367592,0.0002604298,0.0003632102],"domain_scores_gemma":[0.9991634,0.0001208081,0.00006334492,0.0003981863,0.00007680301,0.0001774297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004090922,0.0002188711,0.00000274949,0.0006331245,0.0004277186,0.00003286558,0.0005174081,0.1831888,0.0105638,0.3101903,0.0004944078,0.493689],"study_design_scores_gemma":[0.000464544,0.0004584172,0.000004827835,0.0004595176,0.00008051439,0.0001065768,0.0000663719,0.9904323,0.0002881181,0.001805303,0.005506971,0.0003265098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003417599,0.0007940698,0.9923967,0.0004236584,0.001508408,0.0009519397,0.0000308885,0.0003088284,0.0001679503],"genre_scores_gemma":[0.9966269,0.0002004228,0.0018654,0.00009486563,0.0002206112,0.0004228431,0.00001188281,0.00003741002,0.0005196919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9932093,"threshold_uncertainty_score":0.7669737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030470688436058,"score_gpt":0.2296555581952543,"score_spread":0.2093508513108937,"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."}}