{"id":"W3194398314","doi":"10.1364/cleo_si.2021.aw2e.4","title":"Optical neuromorphic processing based on Kerr microcombs","year":2021,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Neuromorphic engineering; Computer science; Convolutional neural network; Scalability; Perceptron; Field-programmable gate array; Multilayer perceptron; Computer hardware; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Operating system","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":[0.000136048,0.0002413036,0.0002493364,0.00007193012,0.0003033301,0.0005982127,0.0004299318,0.00009865754,0.000009981706],"category_scores_gemma":[0.00003549549,0.0002070316,0.00006502639,0.0003785323,0.00007978144,0.000133576,0.0001597629,0.0004851537,0.00001225141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002217217,"about_ca_system_score_gemma":0.0002728414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001472246,"about_ca_topic_score_gemma":0.000002781046,"domain_scores_codex":[0.9982308,0.00007543886,0.0002220244,0.0006179925,0.0002934803,0.0005602341],"domain_scores_gemma":[0.998979,0.0001524723,0.0000774038,0.0004110781,0.0001667924,0.0002133098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000251776,0.001340194,0.0007803733,0.0003615009,0.00006863612,0.002516033,0.0003123428,0.02533625,0.09897509,0.449513,0.003370493,0.4171743],"study_design_scores_gemma":[0.0004398221,0.000558172,0.0002124548,0.0001591871,0.000009330066,0.00004944964,0.00001191756,0.9754219,0.02024751,0.001948005,0.0006578468,0.0002844236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6016559,0.000451448,0.3475714,0.03143019,0.0007088998,0.0004234421,0.000004080654,0.0004626738,0.01729199],"genre_scores_gemma":[0.9870207,0.00008877806,0.009520832,0.002997831,0.0001162031,0.000005098073,0.000005728668,0.00001633342,0.0002285337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9500856,"threshold_uncertainty_score":0.8442504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873403816036931,"score_gpt":0.2391900395734369,"score_spread":0.2104560014130676,"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."}}