{"id":"W4313006142","doi":"10.1109/ijcnn55064.2022.9892861","title":"Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de Recherche du Québec-Société et Culture; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Firefly algorithm; Computer science; Metric (unit); Artificial neural network; Artificial intelligence; Machine learning; Algorithm; Baseline (sea); Architecture; Data mining; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007435639,0.0004736791,0.0003567157,0.0002976848,0.0008780191,0.0004582436,0.002728746,0.00007721366,0.0005513581],"category_scores_gemma":[0.0000711055,0.0004453188,0.0001555735,0.0009382617,0.0001059031,0.0006681411,0.001497666,0.001685772,0.000007883848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004364234,"about_ca_system_score_gemma":0.0002055341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006849787,"about_ca_topic_score_gemma":0.00003327514,"domain_scores_codex":[0.9945565,0.0004910188,0.0005948307,0.001304836,0.002245193,0.0008076428],"domain_scores_gemma":[0.9976466,0.0003114737,0.000418848,0.001000368,0.0003821999,0.0002404551],"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":[0.00008380366,0.0001100881,0.00002866454,0.000004055443,0.00004512058,0.00004406909,0.0005194782,0.5006226,0.001212816,0.006750789,0.00215889,0.4884196],"study_design_scores_gemma":[0.001371745,0.0004758716,0.0001463505,0.00003720985,0.00001406171,0.0002317369,0.0001998817,0.9931493,0.000129069,0.002556728,0.001208058,0.0004800287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009449659,0.0001057975,0.9693335,0.01419395,0.001117423,0.003708064,0.0001095951,0.0005018363,0.001480194],"genre_scores_gemma":[0.9650428,0.00001077236,0.02480634,0.002704157,0.000561011,0.006077826,0.0002761225,0.00007387721,0.0004471004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9555931,"threshold_uncertainty_score":0.9997998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04243884968605028,"score_gpt":0.2803751729514579,"score_spread":0.2379363232654076,"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."}}