{"id":"W3086005825","doi":"10.1364/cleo_si.2020.sth4m.4","title":"Neural Nets to Approach Optimal Receivers for High Speed Optical Communication","year":2020,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial neural network; Computer science; Photonics; Estimator; Maximum likelihood; Nonlinear system; Silicon photonics; Electronic engineering; Maximum likelihood sequence estimation; Optical communication; Algorithm; Artificial intelligence; Estimation theory; Mathematics; Optoelectronics; Engineering; Physics","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":[0.0006775877,0.0004630849,0.0003244755,0.0003496222,0.0003219404,0.0004947319,0.0004970289,0.001146116,0.001996823],"category_scores_gemma":[0.002511334,0.0003440694,0.0001960398,0.0002667105,0.0006798938,0.000830576,0.0004645289,0.0009048287,0.0002404869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000939502,"about_ca_system_score_gemma":0.0007293938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004252063,"about_ca_topic_score_gemma":0.004848321,"domain_scores_codex":[0.9998228,0.00007043955,0.000004793485,0.00001517445,0.00005864966,0.00002820613],"domain_scores_gemma":[0.9993933,0.0004701379,0.00004874315,0.00001299043,0.00005971417,0.00001510663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001725705,0.00001172603,0.00009553424,0.00001275672,0.00000666493,0.00001215517,0.000009674876,0.9828834,0.0004678674,0.01210365,0.0001479864,0.004231349],"study_design_scores_gemma":[0.000003459502,0.000007343117,0.00001427848,0.000001406695,9.965094e-7,0.000002458231,0.000001476502,0.9966741,0.0001269857,0.003067018,0.00009921862,0.000001358184],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06415172,0.0006283722,0.9254157,0.0004935147,0.0001034686,0.00003989709,0.00002797036,0.0002392178,0.008900131],"genre_scores_gemma":[0.8228939,0.000406147,0.1652926,0.0002091305,0.00008580152,0.0001490348,0.00003971239,0.00006162883,0.01086208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004252063,"threshold_uncertainty_score":0.00845468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933840961941004,"score_gpt":0.229798699404112,"score_spread":0.200460289784702,"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."}}