{"id":"W3037824697","doi":"10.48550/arxiv.2006.13838","title":"Advances in Asynchronous Parallel and Distributed Optimization","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Asynchronous communication; Computer science; Asynchrony (computer programming); Distributed computing; Context (archaeology); Stochastic optimization; Convergence (economics); Optimization problem; Mathematical optimization; Parallel computing; Algorithm; Computer network; Mathematics","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":[0.003189485,0.00115226,0.00112079,0.0008526958,0.0005584134,0.00192295,0.001430745,0.001122084,0.002598748],"category_scores_gemma":[0.00819615,0.0005826553,0.0009400357,0.001275864,0.001494391,0.002656858,0.001952002,0.003306171,0.000877255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184828,"about_ca_system_score_gemma":0.00143615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338916,"about_ca_topic_score_gemma":0.0009482273,"domain_scores_codex":[0.9976579,0.0007684869,0.0001367867,0.0003935463,0.0009309605,0.0001122531],"domain_scores_gemma":[0.9959764,0.002434932,0.0002723012,0.0004831424,0.000714301,0.0001190696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001093086,0.00007624377,0.0006900692,0.0006574433,0.00008460764,0.0001296247,0.0001814788,0.2245219,0.00368006,0.6457522,0.006955355,0.1171616],"study_design_scores_gemma":[0.00004193636,0.00005395351,0.0002432565,0.00007272157,0.00002869351,0.00007435323,0.00002343089,0.7331424,0.001317658,0.2355741,0.02940598,0.00002147893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003235563,0.009419271,0.9734097,0.001663322,0.0005338953,0.00003555404,0.00004712868,0.0001939659,0.01146162],"genre_scores_gemma":[0.395391,0.03972097,0.5413672,0.001397753,0.005033158,0.0004439163,0.000245557,0.0006220599,0.01577845],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003189485,"threshold_uncertainty_score":0.01686782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0401210591726289,"score_gpt":0.1889036506252838,"score_spread":0.1487825914526549,"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."}}