{"id":"W2581521217","doi":"10.1109/isit.2017.8006751","title":"Neural offset min-sum decoding","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Nvidia","keywords":"Decoding methods; Offset (computer science); Computer science; Multiplicative function; Belief propagation; Algorithm; Artificial neural network; Graph; Path (computing); Arithmetic; Theoretical computer science; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006052635,0.0006676399,0.0006598114,0.000571285,0.0004384715,0.001005345,0.001387206,0.00091036,0.003428597],"category_scores_gemma":[0.004175803,0.0002696873,0.0003515782,0.0007403733,0.0007470053,0.001354087,0.001306436,0.001352582,0.00109154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007696687,"about_ca_system_score_gemma":0.001156628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084163,"about_ca_topic_score_gemma":0.004358275,"domain_scores_codex":[0.9993116,0.0001296158,0.00003786019,0.0001504289,0.0002906461,0.00007982484],"domain_scores_gemma":[0.9990091,0.0003549476,0.00005849205,0.0002475237,0.0002948639,0.00003497599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004911353,0.00009310646,0.0009651284,0.0002308793,0.00008994484,0.0001817903,0.0001654475,0.2880311,0.02977445,0.1281685,0.006235163,0.5455734],"study_design_scores_gemma":[0.00001225212,0.00003922271,0.0001360459,0.00001731189,0.00001558916,0.0001142869,0.00001223574,0.9365792,0.02828084,0.03238108,0.002398686,0.0000132804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02046815,0.0002864721,0.9716794,0.0002715457,0.0001264356,0.00002360883,0.0001095589,0.001199765,0.005835112],"genre_scores_gemma":[0.5066035,0.0003456983,0.4763312,0.0003254064,0.000100626,0.00006224391,0.0002969349,0.0002528188,0.01568156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003428597,"threshold_uncertainty_score":0.01146984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06152156936520667,"score_gpt":0.330383403636796,"score_spread":0.2688618342715894,"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."}}