{"id":"W3018848489","doi":"10.1016/j.physd.2020.132721","title":"Memory and forecasting capacities of nonlinear recurrent networks","year":2020,"lang":"en","type":"article","venue":"Physica D Nonlinear Phenomena","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Nanyang Technological University; Universität St. Gallen; Agence Nationale de la Recherche; Ottawa Hospital Research Institute; Division of Mathematical Sciences; National Science Foundation","keywords":"Autocovariance; Echo state network; Controllability; Nonlinear system; Rank (graph theory); Series (stratigraphy); Computer science; Mathematics; Recurrent neural network; State (computer science); Applied mathematics; Control theory (sociology); Artificial neural network; Algorithm; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001489901,0.0002112006,0.0003735599,0.00003765558,0.0001432737,0.00007645966,0.0005717137,0.00003749063,0.000003970147],"category_scores_gemma":[0.00003587954,0.000182406,0.00009941602,0.0004178524,0.0001395934,0.0002624118,0.0006250556,0.0002812775,0.000004008217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001193448,"about_ca_system_score_gemma":0.00003039739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001130228,"about_ca_topic_score_gemma":0.00000147224,"domain_scores_codex":[0.998492,0.00006940393,0.0003799809,0.0004458355,0.0002440413,0.0003687404],"domain_scores_gemma":[0.9989913,0.0001962362,0.0002287412,0.0002999595,0.00009183192,0.0001919706],"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":[0.0001081747,0.0003487446,0.0002747327,0.0005154015,0.000175759,0.00003208556,0.007378875,0.1081916,0.002684249,0.004468232,0.001035824,0.8747864],"study_design_scores_gemma":[0.0003012387,0.0002070168,0.00006045032,0.00005612322,0.000007639652,0.000003368782,0.0001006666,0.9964895,0.0007401999,0.0002134958,0.001608909,0.0002113421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7529545,0.004403228,0.2251491,0.007222731,0.001248803,0.0009292451,0.0000380166,0.0006470378,0.007407287],"genre_scores_gemma":[0.9467722,0.00007951408,0.04945832,0.0005542313,0.003066868,0.000005424427,0.000008611319,0.00002638304,0.00002852146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.888298,"threshold_uncertainty_score":0.7438299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03675735036850392,"score_gpt":0.2276050353840696,"score_spread":0.1908476850155657,"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."}}