{"id":"W2604343608","doi":"10.1007/s11063-017-9615-5","title":"A Brain-Inspired Method of Facial Expression Generation Using Chaotic Feature Extracting Bidirectional Associative Memory","year":2017,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Chaotic; Feature (linguistics); Content-addressable memory; Context (archaeology); Attractor; Feature extraction; Artificial neural network; 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":[0.00008553324,0.0001725985,0.0002199106,0.0001534652,0.0002172366,0.0002008781,0.0004092424,0.0001915062,0.001392114],"category_scores_gemma":[0.0001871253,0.00009088244,0.0002371871,0.0001976381,0.0001896132,0.0003218772,0.000300218,0.0001988166,0.0002371482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000156857,"about_ca_system_score_gemma":0.0001724729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006472061,"about_ca_topic_score_gemma":0.0008278752,"domain_scores_codex":[0.9999584,0.000005575548,0.000002751801,0.00001328937,0.00001374066,0.000006261748],"domain_scores_gemma":[0.9999526,0.0000115722,0.000004656163,0.00001235826,0.00001399678,0.000004861502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002157529,0.00009350562,0.0010876,0.0001503161,0.00008730574,0.0002205147,0.0001428187,0.06506806,0.4766496,0.02572532,0.001917629,0.4286415],"study_design_scores_gemma":[0.00001792023,0.0001171374,0.0008227305,0.000006873453,0.00003598747,0.000242532,0.00002178639,0.9198598,0.07125437,0.004744786,0.002855893,0.00002021401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07380679,0.000332492,0.9197932,0.00009540339,0.00009260855,0.00004885441,0.00004241162,0.0004572411,0.005330975],"genre_scores_gemma":[0.81056,0.0001913326,0.1836653,0.00005423227,0.00002792184,0.00006574501,0.00005431844,0.00004145197,0.005339662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001392114,"threshold_uncertainty_score":0.004657149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06399533764518658,"score_gpt":0.3308956629790223,"score_spread":0.2669003253338358,"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."}}