{"id":"W1608136203","doi":"10.1109/icnn.1994.374339","title":"A new learning algorithm for bidirectional associative memory neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Bidirectional associative memory; Artificial neural network; Content-addressable storage; Associative property; Generalization; Content-addressable memory; Noise (video); Algorithm; Associative learning; Function (biology); Artificial intelligence; Recall; Mathematics; Image (mathematics)","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":[0.0009105977,0.0008137488,0.0008540876,0.001023582,0.0006627776,0.001043942,0.001712853,0.001325755,0.006325198],"category_scores_gemma":[0.002601037,0.0004157889,0.0005910179,0.001085078,0.0005276331,0.001984961,0.001612928,0.0012502,0.001881379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005187612,"about_ca_system_score_gemma":0.000814582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001645845,"about_ca_topic_score_gemma":0.001762982,"domain_scores_codex":[0.9995298,0.00008785859,0.0000390423,0.00009982198,0.0001918668,0.00005157085],"domain_scores_gemma":[0.9994187,0.0001758999,0.00004426704,0.00006904559,0.0002623109,0.00002968487],"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":[0.0001145662,0.00007214357,0.000541775,0.0001930574,0.00006877308,0.00009454324,0.00009164633,0.1465308,0.01066441,0.05887944,0.006713775,0.7760351],"study_design_scores_gemma":[0.00003941817,0.00005036725,0.0001131097,0.00003007184,0.00002087279,0.0001294743,0.00001612933,0.9595261,0.004884611,0.0234199,0.01175064,0.00001925409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002130369,0.0001809236,0.9957527,0.00007192205,0.00007848032,0.00003630258,0.00002744107,0.0003306806,0.001391153],"genre_scores_gemma":[0.06000459,0.0003769285,0.9314905,0.0001570662,0.0001075982,0.0003771173,0.0001961701,0.0002093973,0.007080487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006325198,"threshold_uncertainty_score":0.02115983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165115063527742,"score_gpt":0.2434468317403018,"score_spread":0.2217956811050244,"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."}}