{"id":"W2149653171","doi":"10.1109/iscas.2003.1205061","title":"Three-layer bidirectional asymmetrical associative memory","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Bidirectional associative memory; Content-addressable memory; Computer science; Associative property; Layer (electronics); Artificial neural network; Feed forward; Content-addressable storage; Feedforward neural network; Recall; Recurrent neural network; Pattern recognition (psychology); Artificial intelligence; Mathematics; Psychology","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":[0.0003098232,0.0003364601,0.0003827178,0.000201345,0.000244415,0.0005920979,0.001098816,0.0005218606,0.004374118],"category_scores_gemma":[0.0007239159,0.0001401152,0.0004169859,0.0002408074,0.0003440055,0.001016499,0.0006200316,0.0003931409,0.001232019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894242,"about_ca_system_score_gemma":0.0002874065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000713673,"about_ca_topic_score_gemma":0.000951896,"domain_scores_codex":[0.9998461,0.00002512935,0.00001512677,0.00003813915,0.00004503251,0.00003036235],"domain_scores_gemma":[0.9997432,0.00004190697,0.00003560345,0.00006972904,0.00008223457,0.00002735891],"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":[0.0009098045,0.0002641784,0.003225707,0.000572397,0.0002584916,0.001039463,0.0002849566,0.1762368,0.1716875,0.1357282,0.00599528,0.5037971],"study_design_scores_gemma":[0.00005699776,0.000342337,0.001137908,0.00003957853,0.0001445127,0.0008099544,0.00004749814,0.890361,0.04554901,0.04850281,0.01295485,0.00005345954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1306223,0.0007052525,0.8428096,0.0002372957,0.0002625998,0.00006884053,0.0002338223,0.002114822,0.02294545],"genre_scores_gemma":[0.879778,0.0004408339,0.1089496,0.0001663892,0.00005052067,0.0000726076,0.0002513564,0.0000529651,0.01023768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004374118,"threshold_uncertainty_score":0.01463288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313041036258843,"score_gpt":0.2645455905726757,"score_spread":0.2332414869467914,"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."}}