{"id":"W2922251441","doi":"10.1007/s00500-019-03916-5","title":"Granular autoencoders: concepts and design","year":2019,"lang":"en","type":"article","venue":"Soft Computing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"King Abdulaziz University","keywords":"Granularity; Autoencoder; Computer science; Cluster analysis; Artificial intelligence; Representation (politics); Key (lock); Granular computing; Machine learning; Artificial neural network; Data mining; Rough set","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000377263,0.000120497,0.0001937485,0.0000588097,0.0001715343,0.0002301264,0.0003737407,0.00003919657,0.00002215845],"category_scores_gemma":[0.00003386231,0.0001109827,0.00005591374,0.0002682676,0.00003720166,0.0002907932,0.0003187086,0.0001065337,0.00006859034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001402144,"about_ca_system_score_gemma":0.00002626308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001884901,"about_ca_topic_score_gemma":6.560083e-7,"domain_scores_codex":[0.998912,0.00006090641,0.0001969875,0.0003761218,0.0001652514,0.0002887237],"domain_scores_gemma":[0.9992956,0.0001860255,0.0001008164,0.00029777,0.0000482645,0.00007153256],"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":[0.00001521956,0.00006795852,0.0421988,0.000114442,0.0001024773,0.00005459142,0.009579148,0.1333394,0.002279613,0.08005764,0.001012852,0.7311779],"study_design_scores_gemma":[0.0002107624,0.00005717492,0.00129243,0.00003332135,0.000005506436,0.00001797611,0.0001012306,0.9956388,0.000154535,0.00128833,0.001043269,0.0001566165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04166601,0.0002597813,0.9563286,0.000145448,0.000186148,0.0001064775,1.760879e-7,0.0001776247,0.001129721],"genre_scores_gemma":[0.8131549,0.000002351376,0.186477,0.0001788386,0.00004746218,4.893128e-7,5.732502e-7,0.000006994078,0.0001314307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8622994,"threshold_uncertainty_score":0.4525745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652275673031815,"score_gpt":0.2399982391741039,"score_spread":0.2234754824437858,"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."}}