{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001426092,0.0001563459,0.0001475234,0.00005512509,0.0002729002,0.000221473,0.0002318841,0.000106952,0.00006040834],"category_scores_gemma":[0.000006319234,0.000169799,0.000113931,0.0005447466,0.00003718107,0.0001406537,0.00000352954,0.0002689826,0.0001474041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004634125,"about_ca_system_score_gemma":0.00008806986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001332393,"about_ca_topic_score_gemma":0.00008688692,"domain_scores_codex":[0.9986709,0.00009465368,0.0002369926,0.0003950416,0.0002678018,0.0003346568],"domain_scores_gemma":[0.9989573,0.0001572378,0.00005569186,0.0005627183,0.0001527357,0.0001142842],"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":[0.000004542804,0.0001163967,0.000009551209,0.000008826923,0.00003740494,0.00005860662,0.0006500967,0.7956418,0.0007269136,0.002048707,0.0007590851,0.199938],"study_design_scores_gemma":[0.0006764033,0.0001404873,0.0003139531,0.00008479384,0.00006198442,0.0001648013,0.0001237128,0.9225739,0.06115342,0.004134906,0.01001699,0.0005546015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008731679,0.0001403727,0.9867169,0.0005378363,0.00150515,0.00007386091,0.000001754115,0.0002408476,0.002051607],"genre_scores_gemma":[0.9402682,0.0000555265,0.05738086,0.0007207946,0.0001520902,0.000009598635,0.000002120257,0.00002068105,0.001390135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9315365,"threshold_uncertainty_score":0.6924201,"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."}}