{"id":"W2963188574","doi":"10.1049/el.2019.1719","title":"GenSynth: a generative synthesis approach to learning generative machines for generate efficient neural networks","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Artificial neural network; Generative grammar; Machine learning; Generator (circuit theory); Computer engineering","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":[0.0007636935,0.0008252115,0.0004745206,0.0004970607,0.0003497304,0.0008802037,0.001340307,0.0008476865,0.006839805],"category_scores_gemma":[0.002616078,0.0006042761,0.001030903,0.0003458223,0.001197171,0.0009182181,0.001295553,0.00163833,0.00122609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000680424,"about_ca_system_score_gemma":0.000712787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009222988,"about_ca_topic_score_gemma":0.002507416,"domain_scores_codex":[0.9997367,0.0000778857,0.00001405783,0.00005882968,0.00008348213,0.00002907848],"domain_scores_gemma":[0.9992374,0.00050974,0.00004073872,0.0001188787,0.0000691605,0.00002404077],"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":[0.0000898407,0.00006851153,0.001104962,0.0003028243,0.00009633612,0.0002334871,0.0002141558,0.6542132,0.01614893,0.1796001,0.004867236,0.1430604],"study_design_scores_gemma":[0.00001762592,0.00003970809,0.00005475752,0.00002920726,0.00001630138,0.00006262516,0.00001856601,0.9224885,0.007007836,0.06457767,0.0056773,0.000009880403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004249906,0.0001520677,0.9911503,0.0001331273,0.00003968343,0.00004397719,0.00008092788,0.0009872554,0.003162791],"genre_scores_gemma":[0.222639,0.0004127744,0.7675797,0.0003736632,0.00005941172,0.0004311977,0.0004876234,0.001175058,0.006841593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006839805,"threshold_uncertainty_score":0.02288145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115322495863479,"score_gpt":0.2343316133131257,"score_spread":0.2231783883544909,"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."}}