{"id":"W2230831824","doi":"10.1109/jsac.2015.2504299","title":"Fast List Decoders for Polar Codes","year":2015,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"University of California, San Diego; École Polytechnique Fédérale de Lausanne; University of Illinois at Urbana-Champaign; Centre National de la Recherche Scientifique; Center for Advanced Study, University of Illinois at Urbana-Champaign; David and Lucile Packard Foundation; National Science Foundation","keywords":"Decoding methods; List decoding; Polar code; Sequential decoding; Concatenated error correction code; Low-density parity-check code; Berlekamp–Welch algorithm; Code (set theory)","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":[],"consensus_categories":[],"category_scores_codex":[0.001170042,0.0001670284,0.0002210112,0.000438979,0.0003984554,0.0002845904,0.003542774,0.00009834051,0.000001851476],"category_scores_gemma":[0.001079987,0.0001650335,0.00007962241,0.001104891,0.00007812366,0.0004729631,0.0001982023,0.0008280028,0.00001171439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004051183,"about_ca_system_score_gemma":0.0004727675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001125222,"about_ca_topic_score_gemma":0.001227341,"domain_scores_codex":[0.998278,0.0004131753,0.0004586779,0.0002163427,0.0002948579,0.0003389011],"domain_scores_gemma":[0.9960898,0.0008643495,0.0002808466,0.001637029,0.0009171032,0.000210836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005686888,0.007021355,0.07812066,0.00007255944,0.0005535024,0.0001004154,0.03567904,0.01003477,0.01812489,0.196888,0.4237226,0.2291135],"study_design_scores_gemma":[0.006057782,0.003173068,0.01131587,0.001447075,0.00009969748,0.002388598,0.00158438,0.6392524,0.02508356,0.1461467,0.1608031,0.002647755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02748116,0.00064788,0.9524078,0.01131473,0.0008559645,0.0005606882,0.00001906377,0.000772657,0.00594001],"genre_scores_gemma":[0.7531579,0.0001424755,0.2460009,0.0004620292,0.00006868357,0.00005272907,0.000007692927,0.00002061418,0.00008703273],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7256767,"threshold_uncertainty_score":0.6729872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0810445107494296,"score_gpt":0.3521803789829451,"score_spread":0.2711358682335155,"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."}}