{"id":"W4386002746","doi":"10.1016/j.neunet.2023.08.018","title":"A multilayered bidirectional associative memory model for learning nonlinear tasks","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Government of Ontario","keywords":"Computer science; Bidirectional associative memory; Nonlinear system; Associative property; Artificial neural network; Content-addressable memory; Artificial intelligence; Set (abstract data type); Task (project management); Feature (linguistics); Pattern recognition (psychology); Layer (electronics); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002476241,0.0001726495,0.0001897394,0.00007583365,0.0004958081,0.0001275526,0.0004825656,0.000114477,0.000004091557],"category_scores_gemma":[0.00005073035,0.0001642695,0.0001571533,0.0008109102,0.00003075761,0.0002549229,0.000203681,0.0003700322,0.00002568206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003866736,"about_ca_system_score_gemma":0.00002712305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000766333,"about_ca_topic_score_gemma":0.00001421103,"domain_scores_codex":[0.9985172,0.00005651618,0.0002367307,0.0004791877,0.0002118908,0.0004984565],"domain_scores_gemma":[0.9989191,0.0004607865,0.0001333536,0.0002554493,0.0001191225,0.0001121724],"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":[0.000007453576,0.00002207419,0.0001030464,0.000002472084,0.00001346241,0.000002041338,0.0001115353,0.9498709,0.0001104687,0.0006644357,0.009661614,0.03943045],"study_design_scores_gemma":[0.0003124802,0.00003958136,0.0004161296,0.000006672256,0.000006690872,0.000002757239,0.00001183892,0.9964827,0.00004147809,0.0007298063,0.001767451,0.000182388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03227971,0.00008476967,0.9620572,0.00264874,0.0007237005,0.0006133008,0.00001726726,0.00126846,0.0003068129],"genre_scores_gemma":[0.9727576,0.00005425824,0.02055428,0.0007951671,0.0008581938,0.0003349289,0.00006252121,0.00003520203,0.004547874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9415029,"threshold_uncertainty_score":0.6698716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03571021137848383,"score_gpt":0.2883529110918606,"score_spread":0.2526426997133767,"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."}}