{"id":"W2980003999","doi":"10.48550/arxiv.1910.04920","title":"Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of British Columbia","funders":"","keywords":"Hessian matrix; Mathematics; Convergence (economics); Applied mathematics; Broyden–Fletcher–Goldfarb–Shanno algorithm; Rate of convergence; Parameterized complexity; Mathematical optimization; Algorithm; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003416444,0.0003618739,0.0003784487,0.0004550421,0.0001150821,0.0001642052,0.001109397,0.0003459701,0.0001634931],"category_scores_gemma":[0.00007165856,0.0004247827,0.00009908179,0.0006187501,0.000150779,0.0004275275,0.002023749,0.0005091942,0.00004169593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001633616,"about_ca_system_score_gemma":0.0002020679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003615903,"about_ca_topic_score_gemma":0.00001214646,"domain_scores_codex":[0.9979162,0.0002073963,0.0002618649,0.001199875,0.0001003555,0.0003142916],"domain_scores_gemma":[0.9978995,0.0002534145,0.0003354494,0.001052234,0.0003032888,0.0001560896],"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":[0.00001901488,0.0000496059,0.0002944034,0.00009222332,0.0001095312,0.00001574104,0.000682671,0.6748422,0.00008552032,0.3226337,0.000202809,0.0009725498],"study_design_scores_gemma":[0.0002862192,0.00006212711,0.0004375843,0.00007297946,0.00004678607,0.00001033794,0.00008934677,0.9165988,0.00006294349,0.08188931,0.00002903359,0.0004145495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008901864,0.00007833938,0.9878223,0.00006764419,0.00112101,0.0005658349,0.00001272419,0.0004239958,0.001006309],"genre_scores_gemma":[0.8529297,0.00002962035,0.145257,0.0001374372,0.00002788897,0.00000259077,0.00001568703,0.00002453332,0.001575509],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8440279,"threshold_uncertainty_score":0.9998204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05628495253693658,"score_gpt":0.2337680489202858,"score_spread":0.1774830963833492,"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."}}