{"id":"W3209917595","doi":"10.1145/3458929","title":"DANCE: Distributed Generative Adversarial Networks with Communication Compression","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Internet Technology","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Upload; Overhead (engineering); Bandwidth (computing); Generative grammar; Cloud computing; Distributed computing; Server; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Theoretical computer science; Computer network; World Wide Web; Operating system","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":[0.0008960048,0.0008623847,0.0007454715,0.0003292195,0.0003062962,0.0005839933,0.001424993,0.0007795818,0.002308663],"category_scores_gemma":[0.002013418,0.0003496999,0.000493922,0.0004089323,0.0008807502,0.001020528,0.001403843,0.001601684,0.0004378306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006872772,"about_ca_system_score_gemma":0.0006552581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002171902,"about_ca_topic_score_gemma":0.00271687,"domain_scores_codex":[0.9995882,0.0001401877,0.00001386623,0.0001057923,0.0000978362,0.00005413282],"domain_scores_gemma":[0.9993712,0.0003883404,0.00004998006,0.00008635632,0.00006985209,0.00003428772],"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":[0.00006978219,0.00003540577,0.0003328547,0.00004091625,0.00003237835,0.00007033098,0.0000363533,0.9309953,0.002197146,0.01847879,0.002917274,0.04479352],"study_design_scores_gemma":[0.000005362148,0.00001378403,0.00002500931,0.000002311393,0.000002533864,0.00001451876,0.000002472928,0.9944981,0.0003842607,0.004585328,0.0004638995,0.000002399245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009729982,0.0003710758,0.9847715,0.0003224085,0.00007961015,0.0000453278,0.00007363073,0.0008962397,0.003710211],"genre_scores_gemma":[0.8507307,0.0005530952,0.1393548,0.0006477639,0.0001354096,0.0002047934,0.0003140943,0.0001942106,0.007865184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002308663,"threshold_uncertainty_score":0.007723272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065080110182506,"score_gpt":0.2291860601856989,"score_spread":0.2185352590838738,"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."}}