{"id":"W2963641440","doi":"10.1109/glocom.2018.8647453","title":"Spatial Deep Learning for Wireless Scheduling","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scheduling (production processes); Wireless network; Distributed computing; Deep learning; Job shop scheduling; Artificial neural network; Wireless; Schedule; Dynamic priority scheduling; Wireless sensor network; Reuse; Artificial intelligence; Computer network; Mathematical optimization; Quality of service; Telecommunications; 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":[0.0006632291,0.0007852655,0.0006751434,0.0004123826,0.0002790792,0.0006181558,0.0009343196,0.0009363355,0.00254759],"category_scores_gemma":[0.002633944,0.000444549,0.0004095325,0.0006201137,0.000763612,0.001055138,0.0008588585,0.001430259,0.000337438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001257305,"about_ca_system_score_gemma":0.001155495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007040009,"about_ca_topic_score_gemma":0.009135719,"domain_scores_codex":[0.9997916,0.00006504867,0.00001040842,0.00004439586,0.00004590738,0.00004276885],"domain_scores_gemma":[0.9992504,0.0004620241,0.00007516713,0.00005877595,0.0001183452,0.00003539081],"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":[0.00002608838,0.000020157,0.0002790869,0.00002515239,0.00001460148,0.00001324553,0.0000112241,0.9660434,0.0003963535,0.007244962,0.00089204,0.02503372],"study_design_scores_gemma":[0.000001290618,0.000003207117,0.00001930312,0.000001447136,9.584929e-7,0.000001262056,0.000001299341,0.9965184,0.00009608542,0.003256201,0.00009991751,7.247493e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03254678,0.001010704,0.9607512,0.0008495231,0.000092649,0.00002191308,0.0001579063,0.0006997019,0.003869523],"genre_scores_gemma":[0.8806375,0.0007178277,0.1110165,0.0003697576,0.0001116945,0.0001077909,0.0003678762,0.0001069459,0.006564056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007040009,"threshold_uncertainty_score":0.01399809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008741428967405568,"score_gpt":0.2302768967258364,"score_spread":0.2215354677584308,"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."}}