{"id":"W3087095379","doi":"10.48550/arxiv.2009.09277","title":"Construction of Polar Codes with Reinforcement Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Traverse; Computer science; Code (set theory); Polar code; Algorithm; Sorting; Reinforcement learning; Decoding methods; Polar; Frame (networking); Equivalence (formal languages); Mathematics; Artificial intelligence; Programming language; Telecommunications; Discrete mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001698462,0.0002306052,0.0003186128,0.0002081018,0.00009939411,0.00004853165,0.00109195,0.0001602426,0.000009808719],"category_scores_gemma":[0.00004636033,0.0002541608,0.0001070862,0.0005180055,0.0001404421,0.0002453772,0.001348475,0.0006970798,0.000009048907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001445337,"about_ca_system_score_gemma":0.0002001896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002906485,"about_ca_topic_score_gemma":0.00002301684,"domain_scores_codex":[0.9986662,0.0001147342,0.0001916106,0.0007075405,0.0001194769,0.0002004207],"domain_scores_gemma":[0.9984838,0.00006722966,0.000471294,0.0006808258,0.0002039822,0.00009287102],"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":[0.0001034164,0.00004272107,0.02975594,0.000292161,0.0002070892,0.0002207862,0.001154476,0.5818113,0.0007197743,0.3842031,0.000113772,0.001375455],"study_design_scores_gemma":[0.000472411,0.000574094,0.0007036166,0.0003993463,0.0001144734,0.00002754129,0.0003723183,0.9613723,0.009788686,0.02502782,0.0004339028,0.0007134855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1124518,0.00001442477,0.8831607,0.00007505583,0.0001653211,0.0002030519,0.000001567128,0.0006477211,0.003280391],"genre_scores_gemma":[0.9665025,0.00005276964,0.03316354,0.00002737114,0.00002221275,6.024175e-7,0.000007495439,0.00001394462,0.000209529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8540507,"threshold_uncertainty_score":0.9999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05201346662222839,"score_gpt":0.1870268462632307,"score_spread":0.1350133796410023,"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."}}