{"id":"W2114153178","doi":"","title":"Rate-coded Restricted Boltzmann Machines for Face Recognition","year":2000,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Boltzmann machine; Computer science; Generative model; Facial recognition system; Face (sociological concept); Restricted Boltzmann machine; Feature vector; Detector; Support vector machine; Generative grammar; Artificial neural network","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":[0.0006734219,0.0005414666,0.0006400406,0.0002523585,0.0001766614,0.0005316001,0.001273465,0.0009053524,0.00235189],"category_scores_gemma":[0.002564491,0.0003246286,0.00049144,0.0003985777,0.0006958559,0.001024165,0.0009055475,0.001764799,0.001012118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007989758,"about_ca_system_score_gemma":0.0005131026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001643745,"about_ca_topic_score_gemma":0.001415153,"domain_scores_codex":[0.9997168,0.0001151051,0.00001068341,0.00004984357,0.00007722898,0.00003026304],"domain_scores_gemma":[0.9994443,0.0003529456,0.0000445634,0.0000781447,0.00005993632,0.00002007091],"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":[0.00005098455,0.00003237189,0.0002688831,0.0000926667,0.00004121644,0.00004029043,0.00004494234,0.8159549,0.0025309,0.09752043,0.003318781,0.08010358],"study_design_scores_gemma":[0.000003369819,0.000007407323,0.00003454857,0.000005074628,0.000003099881,0.000012923,0.000001975571,0.964702,0.0005182661,0.03367344,0.001032833,0.000004984979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003605214,0.001222006,0.9919003,0.000324238,0.00006331135,0.00001936423,0.00004605217,0.0005420156,0.002277575],"genre_scores_gemma":[0.530795,0.002915589,0.450467,0.0004968548,0.0002707719,0.0003684162,0.0003179919,0.0002833138,0.0140852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00235189,"threshold_uncertainty_score":0.007867813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298331958288254,"score_gpt":0.2413044029940622,"score_spread":0.2183210834111796,"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."}}