{"id":"W4388040516","doi":"10.1109/pimrc56721.2023.10294039","title":"Score-Based Generative Modeling for MIMO Detection Without Knowledge of Noise Statistics","year":2023,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noise (video); Probability density function; Prior probability; Computer science; Detector; Benchmark (surveying); MIMO; Algorithm; Gamma distribution; Noise measurement; Gaussian noise; Artificial intelligence; Mathematics; Statistics; Bayesian probability; Noise reduction","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":[0.0001810257,0.00007721141,0.0001090977,0.0001164518,0.0001073338,0.00005850054,0.0001920433,0.00002943289,0.000002279723],"category_scores_gemma":[0.00006994302,0.00006734809,0.00003099582,0.000392607,0.00001727894,0.0001543967,0.00004227613,0.00003697886,0.00001415145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002033132,"about_ca_system_score_gemma":0.0001332397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001264982,"about_ca_topic_score_gemma":0.0001019876,"domain_scores_codex":[0.999361,0.00001604429,0.000160727,0.0002052018,0.0000893685,0.0001675961],"domain_scores_gemma":[0.9994523,0.00007605446,0.00005492922,0.000152227,0.0002216792,0.00004279239],"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":[0.00005458673,0.0001170241,0.0006775082,0.0003376438,0.00003398766,0.000002551969,0.001668362,0.1553619,0.3200118,0.003537098,0.0008503768,0.5173472],"study_design_scores_gemma":[0.0001889316,0.0000433174,0.00001834269,0.00001391493,0.00000280934,2.553051e-7,0.00001634726,0.6032837,0.394145,0.002213476,0.00002030836,0.00005361472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0523156,0.00002803599,0.9469204,0.00005946403,0.0001699851,0.0001304028,0.000007975141,0.0001642631,0.0002038627],"genre_scores_gemma":[0.6090875,0.000001562208,0.3906358,0.00004210879,0.00003539114,0.00001651318,0.000003526319,0.000005685168,0.0001719823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5567719,"threshold_uncertainty_score":0.2746375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05225176864251414,"score_gpt":0.3055193034458457,"score_spread":0.2532675348033316,"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."}}