{"id":"W2795416201","doi":"10.1016/j.chemolab.2018.04.001","title":"Noisy matrix completion on a novel neural network framework","year":2018,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; University of South Florida","keywords":"Overfitting; Early stopping; Missing data; Computer science; Artificial neural network; Matrix completion; Regularization (linguistics); Bayesian probability; Generalization; Algorithm; Artificial intelligence; Iterative method; Data mining; Machine learning; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001135136,0.0008028119,0.0008697009,0.0006046428,0.0003659455,0.0009122012,0.001675552,0.001258283,0.002605691],"category_scores_gemma":[0.003037847,0.000402452,0.0005439944,0.0009376321,0.001107394,0.001898952,0.001663248,0.001513989,0.0005950139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005919887,"about_ca_system_score_gemma":0.00101039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004749543,"about_ca_topic_score_gemma":0.005631872,"domain_scores_codex":[0.9994006,0.0002191662,0.00002447175,0.0001141726,0.0002011161,0.00004054342],"domain_scores_gemma":[0.9989366,0.0004855817,0.0001122654,0.0001505226,0.0002505441,0.00006449421],"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":[0.0001164876,0.00008891538,0.0002803647,0.0001240314,0.00004960757,0.00009680546,0.00005671324,0.7671203,0.006295753,0.149732,0.003643676,0.07239529],"study_design_scores_gemma":[0.000002909638,0.000008678732,0.00002799003,0.000002188181,0.000002629421,0.000009467672,0.000002075856,0.9884971,0.0002965815,0.01077054,0.0003758782,0.000003886758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003983194,0.0001383525,0.994343,0.0001897902,0.00004359899,0.00001301826,0.0000685239,0.00009028427,0.001130171],"genre_scores_gemma":[0.3235759,0.001003271,0.6594169,0.0002449014,0.0005605668,0.0001779241,0.0005753974,0.0001933247,0.01425182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004749543,"threshold_uncertainty_score":0.00944376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02862607974798869,"score_gpt":0.2631497022046758,"score_spread":0.2345236224566871,"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."}}