{"id":"W4225620683","doi":"10.1109/cdc45484.2021.9682985","title":"L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method","year":2021,"lang":"en","type":"article","venue":"2021 60th IEEE Conference on Decision and Control (CDC)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; National Science Foundation","keywords":"Asynchronous communication; Computer science; Node (physics); Convergence (economics); Computation; Dimension (graph theory); Distributed memory; Mathematical optimization; Distributed algorithm; Theoretical computer science; Distributed computing; Algorithm; Parallel computing; Shared memory; Mathematics; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006787732,0.0004062396,0.0006183872,0.0002493461,0.000283759,0.0007386206,0.0009105928,0.0002437686,0.0003552607],"category_scores_gemma":[0.0006673443,0.0003751706,0.0001364654,0.0007176424,0.00009730522,0.0005471772,0.0001862242,0.0003575179,0.00008023805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008995289,"about_ca_system_score_gemma":0.0003826524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002346893,"about_ca_topic_score_gemma":0.0000289196,"domain_scores_codex":[0.9965314,0.000425234,0.0006136887,0.001223498,0.0006927248,0.0005134154],"domain_scores_gemma":[0.9962814,0.0008643837,0.0002352625,0.001368504,0.0007992889,0.0004511786],"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":[0.0001882864,0.0006857933,0.0000948224,0.00001432379,0.00007096157,0.0002446861,0.0003834625,0.001586681,0.005102118,0.1469381,0.002413589,0.8422772],"study_design_scores_gemma":[0.003701208,0.001031727,0.001170461,0.0001924905,0.00005133004,0.0001264098,0.0001963198,0.9605635,0.007888865,0.02239654,0.001953136,0.000727999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003088234,0.0002296552,0.9919231,0.002110836,0.0007471867,0.0003813223,0.00006734136,0.0003104792,0.001141881],"genre_scores_gemma":[0.804957,0.0002414552,0.1921537,0.001862251,0.0001428792,0.00009840962,0.00006376092,0.00003355266,0.00044706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9589768,"threshold_uncertainty_score":0.99987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02599025233896078,"score_gpt":0.2967369035782201,"score_spread":0.2707466512392593,"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."}}