{"id":"W2889576094","doi":"","title":"Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization.","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Variance reduction; Gradient descent; Combinatorics; Nabla symbol; Stationary point; Function (biology); Stochastic gradient descent; Applied mathematics; Mathematical analysis; Computer science; Physics; Statistics; Omega","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":[0.001373686,0.001649598,0.001542335,0.0005421338,0.0004383829,0.0009066197,0.001292051,0.001401556,0.001893755],"category_scores_gemma":[0.003731821,0.0007623142,0.001212968,0.0006235393,0.001184697,0.001137239,0.001356108,0.002117727,0.0007528597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207564,"about_ca_system_score_gemma":0.001890871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006597762,"about_ca_topic_score_gemma":0.008562855,"domain_scores_codex":[0.9992462,0.0003190605,0.00003118412,0.0001284372,0.0002058708,0.00006923774],"domain_scores_gemma":[0.9987977,0.0006911412,0.0001222485,0.00008997553,0.000225717,0.0000731613],"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":[0.00004631155,0.00004036,0.0004834513,0.0001517532,0.00007267822,0.00009201048,0.00004288955,0.9478576,0.001494524,0.02357722,0.003161846,0.02297935],"study_design_scores_gemma":[0.000003123154,0.000008384971,0.00002828029,0.000003813294,0.000002059909,0.000006642351,0.000001958264,0.9963439,0.0001274637,0.003110013,0.000362008,0.00000233578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003946965,0.0004103611,0.9937571,0.0002069781,0.00004283264,0.00003170019,0.00003941704,0.0002017182,0.00136282],"genre_scores_gemma":[0.3432059,0.001025295,0.6449481,0.0005405983,0.0001807753,0.0004282949,0.0006559243,0.00050307,0.008512152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006597762,"threshold_uncertainty_score":0.01311868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298046378812457,"score_gpt":0.2619998012515888,"score_spread":0.2390193374634642,"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."}}