{"id":"W2772031001","doi":"10.15353/vsnl.v3i1.162","title":"Design space exploration of Convolutional Neural Networks based on Evolutionary Algorithms","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperparameter; MNIST database; Convolutional neural network; Computer science; Genetic algorithm; Artificial intelligence; Traverse; Pattern recognition (psychology); Design space exploration; Digit recognition; Machine learning; Algorithm; Space (punctuation); Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005604032,0.0001208165,0.0002191602,0.0001491692,0.0004177905,0.0003205983,0.0004351383,0.00003096193,0.000001698845],"category_scores_gemma":[0.00004086128,0.00009734283,0.00007673068,0.0001003133,0.0001039702,0.001267762,0.00006493925,0.0001545974,0.000001309581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003432717,"about_ca_system_score_gemma":0.00008780744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006865797,"about_ca_topic_score_gemma":6.496512e-8,"domain_scores_codex":[0.9985744,0.0001384953,0.0004930905,0.0001535735,0.0005133552,0.0001270272],"domain_scores_gemma":[0.9977859,0.00038907,0.0009564193,0.0002315408,0.0005385333,0.00009852257],"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":[0.00002611052,0.00006051656,0.0003840264,0.000008203229,0.000008792021,0.000005899884,0.00002879052,0.9754791,0.00005150309,0.01391696,0.002645932,0.007384161],"study_design_scores_gemma":[0.0005645362,0.0001532863,0.01305618,0.0001429242,0.000006813218,0.0000924557,0.00001731331,0.9817852,0.000009909029,0.003758961,0.0003211913,0.00009126002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001268383,0.0004633403,0.9926221,0.004722605,0.0006911957,0.0001448004,0.000002372324,0.00001472771,0.00007048109],"genre_scores_gemma":[0.9574409,0.00001541002,0.04212534,0.0001371675,0.0002477202,0.00000392931,0.000003502378,0.000006528174,0.00001954673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9561725,"threshold_uncertainty_score":0.3969525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235407918901075,"score_gpt":0.2956227826878107,"score_spread":0.2632687034987999,"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."}}