{"id":"W2913966198","doi":"10.1016/j.sigpro.2019.02.010","title":"Accelerated stochastic gradient descent with step size selection rules","year":2019,"lang":"en","type":"article","venue":"Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Stochastic gradient descent; PROX; Computer science; Gradient descent; Gradient method; Convergence (economics); Artificial intelligence; Algorithm; Mathematical optimization; Mathematics; Artificial neural network; Chemistry","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":[0.002228861,0.00101352,0.001477122,0.0007011737,0.0004262759,0.0008420211,0.001741451,0.001990733,0.00446333],"category_scores_gemma":[0.009086574,0.0008505856,0.0006437568,0.0007316641,0.0008906378,0.001013687,0.001325116,0.002162795,0.001573434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004400025,"about_ca_system_score_gemma":0.001461011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002110453,"about_ca_topic_score_gemma":0.003872563,"domain_scores_codex":[0.9989717,0.0003740676,0.00004988766,0.0001118303,0.0004315131,0.00006099071],"domain_scores_gemma":[0.9964592,0.002085974,0.0001747856,0.0003441118,0.0007776541,0.0001582439],"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":[0.000239636,0.0002078696,0.0006165532,0.0001539169,0.0001015674,0.0001460313,0.0000476711,0.7672255,0.005984524,0.05892291,0.009463331,0.1568905],"study_design_scores_gemma":[0.00001616639,0.00001593592,0.0000311533,0.000003285632,0.000003205214,0.00001143666,8.177665e-7,0.9967023,0.0003840365,0.00241012,0.0004187286,0.000002861851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003650661,0.00008931842,0.9946551,0.0001214118,0.00009179047,0.00004381177,0.00002802435,0.0003026569,0.001017244],"genre_scores_gemma":[0.1638307,0.0002571736,0.8242891,0.0003259199,0.0002938242,0.0005018793,0.0002843437,0.000409644,0.009807413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00446333,"threshold_uncertainty_score":0.01493138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01342216422020801,"score_gpt":0.215019254110561,"score_spread":0.201597089890353,"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."}}