{"id":"W3120098425","doi":"10.36227/techrxiv.16973650.v1","title":"Random Fourier Feature Based Deep Learning for Wireless Communications","year":2021,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Kernel (algebra); Wireless; Fourier transform; Artificial intelligence; Convergence (economics); Feature (linguistics); Algorithm; Pattern recognition (psychology); Telecommunications; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001813968,0.00007019382,0.0001075372,0.00002990771,0.000360791,0.000244882,0.0006342886,0.00004532692,0.00002137276],"category_scores_gemma":[0.0001277285,0.00006227943,0.00006943092,0.0002733358,0.0000243786,0.0002147696,0.0001675921,0.0001438758,0.000009443174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001346709,"about_ca_system_score_gemma":0.0001126043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001473574,"about_ca_topic_score_gemma":0.00002741141,"domain_scores_codex":[0.9993791,0.00005855401,0.00008797103,0.0001932298,0.0001086346,0.0001725276],"domain_scores_gemma":[0.998806,0.0003143786,0.00004389473,0.0006038168,0.0001773752,0.00005455238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002439695,0.0001199844,0.001228064,0.00005932173,0.0000361967,0.00001321663,0.0004938865,0.000817921,0.01780277,0.01080828,0.004226524,0.9643694],"study_design_scores_gemma":[0.001989292,0.00002143158,0.0001139834,0.00003547858,0.000009848139,0.00001176384,0.00009086289,0.7344302,0.1638022,0.001540165,0.09776688,0.0001878668],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005677849,0.0006260752,0.9832408,0.01023442,0.00007620478,0.00007720686,3.940713e-7,0.0001507702,0.005026306],"genre_scores_gemma":[0.1612875,0.00001861995,0.8340777,0.001519161,0.00003763119,0.00002877462,0.00001691054,0.000007348228,0.003006348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9641815,"threshold_uncertainty_score":0.277495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902044933677391,"score_gpt":0.2687987280758551,"score_spread":0.2497782787390812,"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."}}