{"id":"W2808092577","doi":"10.24963/ijcai.2018/299","title":"Faster Training Algorithms for Structured Sparsity-Inducing Norm","year":2018,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; National Institutes of Health; National Science Foundation","keywords":"Algorithm; Regularization (linguistics); Computer science; Operator (biology); Norm (philosophy); Rate of convergence; Generalization; Convergence (economics); Mathematics; Key (lock); Artificial intelligence","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":[0.001857961,0.001307316,0.001220755,0.001013672,0.0005070831,0.0009246154,0.001658944,0.001480837,0.006635183],"category_scores_gemma":[0.008144819,0.0005406373,0.0008855458,0.001264491,0.0009682836,0.002455632,0.001855787,0.003285224,0.002074007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008038902,"about_ca_system_score_gemma":0.001771043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002905402,"about_ca_topic_score_gemma":0.004133022,"domain_scores_codex":[0.9987741,0.0003332127,0.00007154176,0.0002422855,0.0004793914,0.0000995102],"domain_scores_gemma":[0.997633,0.001154925,0.0001694967,0.0003776138,0.0005687922,0.00009620381],"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":[0.0002180283,0.0001883322,0.0008751593,0.0003131754,0.00007867703,0.0001194684,0.0002294015,0.410356,0.01097982,0.09996436,0.01296039,0.4637172],"study_design_scores_gemma":[0.00001697815,0.00002120559,0.00006876119,0.000009608047,0.000004346566,0.00002793871,0.00001134505,0.9848038,0.001389617,0.01190976,0.001730234,0.00000630072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001629463,0.00008949685,0.9969797,0.00009502572,0.00003383151,0.00003177049,0.00002632131,0.0003419922,0.0007725239],"genre_scores_gemma":[0.0691222,0.0003405211,0.9257035,0.0002341546,0.000153894,0.0002958919,0.0004237352,0.0002718944,0.00345407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006635183,"threshold_uncertainty_score":0.02219689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0571724760049361,"score_gpt":0.2664896888511191,"score_spread":0.209317212846183,"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."}}