{"id":"W3035946931","doi":"10.48550/arxiv.2006.11077","title":"A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Bottleneck; Computer science; Overhead (engineering); Gas compressor; Transformation (genetics); Key (lock); Communication complexity; Distributed computing; Computer engineering; Mathematical optimization; Theoretical computer science; Mathematics; Embedded system; Engineering","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":[0.001912823,0.0006849581,0.0009273792,0.0004656996,0.0005805284,0.0008962486,0.001334971,0.001338482,0.002951486],"category_scores_gemma":[0.008366959,0.0002440331,0.0003533658,0.0007456679,0.001346102,0.003475518,0.002283963,0.002459913,0.0004751078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007034681,"about_ca_system_score_gemma":0.00136768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008102182,"about_ca_topic_score_gemma":0.0008866616,"domain_scores_codex":[0.9983326,0.0006275807,0.00006718216,0.0002969827,0.0005525893,0.000123103],"domain_scores_gemma":[0.9973247,0.001341349,0.0001967621,0.0006788465,0.0003236229,0.0001347134],"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":[0.00055067,0.0002248155,0.0009604426,0.0002410457,0.00006558523,0.000193877,0.0002807889,0.526961,0.01776632,0.2861287,0.0054544,0.1611723],"study_design_scores_gemma":[0.00004351278,0.0001082517,0.0001045391,0.00001735775,0.000007210925,0.00004876878,0.00002612609,0.9373959,0.005032364,0.05421198,0.002990569,0.00001335312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01387645,0.0002525649,0.9826841,0.0008960944,0.00006974854,0.00003730829,0.00003619183,0.0004239462,0.001723629],"genre_scores_gemma":[0.6901966,0.0004012354,0.3027301,0.0005707269,0.0002508388,0.0001903007,0.0001469686,0.0001514899,0.005361816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002951486,"threshold_uncertainty_score":0.01011604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09560234657398782,"score_gpt":0.2252310290454543,"score_spread":0.1296286824714665,"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."}}