{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006583365,0.0004948869,0.0004115188,0.0003615599,0.0001375085,0.0005812509,0.0007419367,0.0006519051,0.002061384],"category_scores_gemma":[0.002473602,0.0001970779,0.000261154,0.0005924315,0.0004251928,0.001208877,0.0006317577,0.001181756,0.0006012637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006553021,"about_ca_system_score_gemma":0.0004775882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001994259,"about_ca_topic_score_gemma":0.002266889,"domain_scores_codex":[0.9998052,0.00005235953,0.00000863618,0.00003085782,0.00006814242,0.00003490433],"domain_scores_gemma":[0.9995337,0.0002739585,0.00004062441,0.00005302127,0.00008476695,0.00001409675],"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":[0.0001565821,0.00009915207,0.001007168,0.0002080133,0.00006895529,0.000154634,0.00004818766,0.5743783,0.0095856,0.1312404,0.006647728,0.2764052],"study_design_scores_gemma":[0.000002211585,0.00001289023,0.0001041052,0.000006498114,0.000003711054,0.00002280373,0.000003198403,0.9872059,0.001082673,0.01060812,0.0009442676,0.000003626488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01092171,0.0008330351,0.9850236,0.0003386516,0.00005694792,0.0000123516,0.00008940968,0.000380283,0.002344026],"genre_scores_gemma":[0.7899718,0.001959627,0.1934451,0.0003166747,0.0001572994,0.00008721254,0.0004121226,0.0001600017,0.0134901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002061384,"threshold_uncertainty_score":0.006896079,"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."}}