{"id":"W4388863424","doi":"10.1007/s10489-023-05031-3","title":"Algorithmic generalization ability of PALM for double sparse regularized regression","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustification; Mathematical optimization; Overfitting; Outlier; Regularization (linguistics); Linearization; Algorithm; Artificial intelligence; Machine learning; Mathematics; Artificial neural network","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":[0.002362115,0.0007658807,0.001012131,0.0009444702,0.000459226,0.001339538,0.001173397,0.001052575,0.003775795],"category_scores_gemma":[0.01255848,0.0004271181,0.0008484776,0.0007327952,0.001389334,0.003314798,0.002531837,0.002076858,0.0008200114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004431082,"about_ca_system_score_gemma":0.0008276709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001097761,"about_ca_topic_score_gemma":0.00103455,"domain_scores_codex":[0.9989663,0.0003158057,0.00005804217,0.0002760826,0.0002722083,0.000111528],"domain_scores_gemma":[0.994921,0.002983448,0.0003406735,0.001019109,0.0005587633,0.0001770414],"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":[0.0004504477,0.0001540133,0.002656774,0.0003710374,0.0001452772,0.0002931578,0.0002663599,0.4061085,0.01448207,0.2779851,0.006508559,0.2905788],"study_design_scores_gemma":[0.000008637674,0.00004620489,0.0002751666,0.00001282853,0.00001223914,0.00008911052,0.00002220481,0.9288732,0.00172858,0.06795792,0.0009633945,0.00001038086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02545492,0.0003036563,0.970235,0.0003755708,0.00004359366,0.00002669777,0.00009024043,0.0003187874,0.003151558],"genre_scores_gemma":[0.6573588,0.001136467,0.3311994,0.0003884932,0.0002367261,0.0001478851,0.0005470876,0.0002436626,0.008741422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003775795,"threshold_uncertainty_score":0.0126313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03990019454984641,"score_gpt":0.2829246408834553,"score_spread":0.2430244463336088,"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."}}