{"id":"W4312522780","doi":"10.1364/networks.2022.new2d.2","title":"Silicon Photonics Neural Networks for Training and Inference","year":2022,"lang":"en","type":"article","venue":"Optica Advanced Photonics Congress 2022","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Queen's University","funders":"","keywords":"Photonics; Computer science; Artificial neural network; Silicon photonics; Computer architecture; Deep learning; Inference; Electronics; Electronic engineering; Artificial intelligence; Engineering; Electrical engineering; Materials science; Optoelectronics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004992284,0.0003155601,0.0004211371,0.0001032061,0.001177531,0.000281393,0.001291816,0.00006646409,0.00003918709],"category_scores_gemma":[0.00009660039,0.000327297,0.0001359948,0.0005589371,0.0001029637,0.0004295056,0.001792962,0.0007348996,5.087447e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008540376,"about_ca_system_score_gemma":0.0001062375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004828824,"about_ca_topic_score_gemma":0.000008817388,"domain_scores_codex":[0.9973233,0.0001191694,0.0004248313,0.0008962457,0.0003905469,0.000845886],"domain_scores_gemma":[0.9978381,0.0008848289,0.0002468448,0.0006975562,0.0001093629,0.0002232777],"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":[0.00006004033,0.00005762877,0.00007045099,0.00002466144,0.00003172526,0.00004791067,0.0004270469,0.8937933,0.000757178,0.02708149,0.0001159902,0.07753261],"study_design_scores_gemma":[0.0009077469,0.0003944531,0.00002285279,0.00001658589,0.00001203748,0.00006865431,0.0001844713,0.9750758,0.0002012793,0.001719038,0.02099086,0.000406218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5826218,0.01020317,0.3880954,0.00257271,0.01011906,0.003541209,0.00009355008,0.0009628167,0.001790323],"genre_scores_gemma":[0.9349787,0.0002679084,0.06295227,0.0009267888,0.00004324503,0.0004832184,0.00002341006,0.0000469588,0.0002774483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.352357,"threshold_uncertainty_score":0.9999179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985685363263334,"score_gpt":0.2677064003011115,"score_spread":0.2478495466684782,"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."}}