{"id":"W2951500940","doi":"10.48550/arxiv.1812.09404","title":"Derandomized Distributed Multi-resource Allocation with Little Communication Overhead","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Resource allocation; Computer science; Overhead (engineering); The Internet; Multiplicative function; Resource (disambiguation); Distributed computing; Mathematical optimization; 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":[0.0003479596,0.0003159635,0.0003564985,0.0002358513,0.0002482838,0.000179264,0.002032544,0.00026144,0.00001824288],"category_scores_gemma":[0.000109488,0.0003295598,0.0001162884,0.0006468487,0.0002323354,0.000357682,0.001381665,0.0003760878,0.00003020115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002850512,"about_ca_system_score_gemma":0.000160399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001147668,"about_ca_topic_score_gemma":0.00003584534,"domain_scores_codex":[0.9981896,0.0002828483,0.0002532428,0.0008700882,0.000130673,0.0002735201],"domain_scores_gemma":[0.9966846,0.0002165894,0.0004472214,0.002067566,0.0004496692,0.000134366],"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":[0.000961178,0.0007452549,0.002021197,0.0001338344,0.000435601,0.00005207293,0.00145693,0.7244014,0.00004530405,0.2651132,0.003587009,0.001046984],"study_design_scores_gemma":[0.003491926,0.00007389113,0.0004209262,0.0001957256,0.00007911883,0.000004869316,0.00003902101,0.9847391,0.0003252338,0.009854595,0.0003270174,0.0004485796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01478834,0.00004877062,0.9827926,0.00019059,0.00009589994,0.0007496831,0.00002559775,0.0007759353,0.0005326346],"genre_scores_gemma":[0.8880059,0.00006177409,0.111237,0.00007473164,0.00002264819,0.000009347204,0.0002870685,0.00002265795,0.0002789628],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8732175,"threshold_uncertainty_score":0.9999157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0568905815199296,"score_gpt":0.1971280292306877,"score_spread":0.1402374477107581,"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."}}