{"id":"W4238695541","doi":"10.36227/techrxiv.12089358","title":"Optimizing Convolutional Neural Network Parameters for Better Image Classification","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"MNIST database; Computer science; Convolutional neural network; Artificial intelligence; Image (mathematics); Transfer of learning; Set (abstract data type); Pattern recognition (psychology); Convergence (economics); Contextual image classification; Cloud computing; Task (project management); Machine learning; Visualization; Data set; Range (aeronautics); Artificial neural network; Overfitting; Data mining","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":[0.0009496368,0.002319403,0.0008221521,0.0008044607,0.0003574774,0.001378586,0.001232901,0.001850113,0.005435972],"category_scores_gemma":[0.004971974,0.0004195193,0.0006271283,0.001012651,0.0004691214,0.002059201,0.0007411767,0.002418323,0.00305609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093951,"about_ca_system_score_gemma":0.00093969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006717619,"about_ca_topic_score_gemma":0.008894739,"domain_scores_codex":[0.9994524,0.00009449466,0.00003164927,0.0002079455,0.0001097265,0.000103686],"domain_scores_gemma":[0.9991776,0.0002949345,0.0000734456,0.0002224903,0.0001967357,0.00003469172],"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":[0.0003303187,0.0003576358,0.002205066,0.0002986877,0.0001962872,0.0001784845,0.00007228638,0.5391027,0.04913558,0.004850545,0.01544257,0.3878298],"study_design_scores_gemma":[0.00001425251,0.0000287893,0.0004660656,0.00001858956,0.00002075358,0.00004340861,0.00001225505,0.973056,0.02138893,0.003116127,0.001820987,0.00001386667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1405928,0.002544324,0.8327162,0.0009433969,0.0004750479,0.0001430668,0.0007980663,0.01255647,0.009230615],"genre_scores_gemma":[0.7064213,0.0007148665,0.2798986,0.0003868365,0.00009369561,0.000171243,0.001805921,0.001421002,0.009086592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006717619,"threshold_uncertainty_score":0.01818514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0534987267367867,"score_gpt":0.2892161410253321,"score_spread":0.2357174142885454,"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."}}