{"id":"W3134822772","doi":"10.1109/tcyb.2021.3054878","title":"DLIN: Deep Ladder Imputation Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Missing data; Imputation (statistics); Computer science; Data mining; Benchmark (surveying); Artificial intelligence; Generalization; Flexibility (engineering); Artificial neural network; Machine learning; Algorithm; Mathematics; Statistics","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":[0.002104258,0.0007974165,0.00119152,0.0008549262,0.0006324147,0.001010915,0.003005894,0.001448266,0.004367306],"category_scores_gemma":[0.004558787,0.0005871396,0.0008262718,0.001166771,0.0006025149,0.001746118,0.00226357,0.002431005,0.001552024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495245,"about_ca_system_score_gemma":0.001816903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008226953,"about_ca_topic_score_gemma":0.01230875,"domain_scores_codex":[0.9993132,0.0001863288,0.00004051228,0.0001729278,0.0001706672,0.0001163227],"domain_scores_gemma":[0.9988936,0.0004314137,0.000128623,0.0001860503,0.0002866424,0.00007376196],"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":[0.0004907982,0.0002803838,0.004390067,0.0002671846,0.00021049,0.0002876534,0.0001321349,0.4053394,0.002753771,0.01975204,0.02349379,0.5426022],"study_design_scores_gemma":[0.00001877088,0.00004451871,0.0002972927,0.00002746914,0.00001746701,0.00005079146,0.00001758912,0.9853615,0.001157101,0.009645506,0.003349985,0.00001200247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008828973,0.0006755024,0.9839679,0.000610953,0.0001244025,0.00007084915,0.0005890175,0.002562998,0.002569234],"genre_scores_gemma":[0.4432502,0.001305921,0.5318597,0.001737592,0.0001989621,0.000557556,0.004809022,0.0003953114,0.01588573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008226953,"threshold_uncertainty_score":0.01635814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131036354069623,"score_gpt":0.2265366647488059,"score_spread":0.2152263012081096,"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."}}