{"id":"W4387369393","doi":"10.1016/j.patcog.2023.109992","title":"Matrix randomized autoencoder","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Provincial Universities of Zhejiang; National Key Research and Development Program of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Autoencoder; Computer science; Scalar (mathematics); Matrix (chemical analysis); Representation (politics); Column (typography); Code (set theory); Class (philosophy); Pattern recognition (psychology); Tensor (intrinsic definition); Algorithm; Artificial intelligence; Mathematics; Deep learning","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":[0.0004967878,0.0005674449,0.0006506679,0.0002678084,0.0003475741,0.0006153865,0.00071399,0.001048408,0.00858478],"category_scores_gemma":[0.001405847,0.0003032462,0.0004544608,0.0003512233,0.0003683345,0.000780347,0.0007522122,0.001242688,0.004537555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003675384,"about_ca_system_score_gemma":0.0009962008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003336135,"about_ca_topic_score_gemma":0.005459208,"domain_scores_codex":[0.9996592,0.00007057332,0.00001630486,0.0001150245,0.00009828925,0.00004061816],"domain_scores_gemma":[0.9995858,0.0001107893,0.00001844145,0.0001212234,0.0001453277,0.00001833846],"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":[0.0002904771,0.000135411,0.0005601169,0.0001167286,0.00009972283,0.00009184524,0.00003109944,0.2272376,0.02409871,0.05003925,0.02343946,0.6738595],"study_design_scores_gemma":[0.00001217998,0.00004498158,0.0001825474,0.00001069819,0.00001410437,0.00004924155,0.000005776358,0.9794899,0.00811821,0.006122191,0.005940401,0.000009756918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008077189,0.0006656964,0.9827747,0.0004089497,0.0004094851,0.00005536363,0.0002836136,0.0018982,0.005426796],"genre_scores_gemma":[0.3576183,0.0008849737,0.5977691,0.0007606335,0.000434545,0.0002183934,0.001776614,0.0002767819,0.0402607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00858478,"threshold_uncertainty_score":0.02871889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164062769398055,"score_gpt":0.2823863829962465,"score_spread":0.260745755302266,"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."}}