{"id":"W4249565363","doi":"10.32920/ryerson.14650011","title":"Effects of Parameter Change on Neural Network and Deep Neural Network","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Noise (video); Computation; Computer science; Gaussian noise; Decision boundary; Simple (philosophy); Artificial intelligence; MATLAB; Time delay neural network; Algorithm; Stochastic neural network; Boundary (topology); Gaussian; Pattern recognition (psychology); Mathematics; Support vector machine; Physics","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":[0.002539244,0.00129716,0.0007689221,0.0008261711,0.0005546403,0.001502146,0.001199235,0.001997204,0.005603858],"category_scores_gemma":[0.05452703,0.0004703807,0.0006389831,0.001109905,0.001412272,0.003170147,0.001442499,0.002612343,0.000827592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009653033,"about_ca_system_score_gemma":0.0005292378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993785,"about_ca_topic_score_gemma":0.00203425,"domain_scores_codex":[0.997449,0.0006759566,0.0003146633,0.0006763128,0.0005915324,0.0002925629],"domain_scores_gemma":[0.9738435,0.01999729,0.0008960858,0.002966683,0.001886716,0.0004096531],"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":[0.003371154,0.0007311166,0.01140226,0.001653813,0.0004198163,0.001455466,0.0006485768,0.7095105,0.07561646,0.01223988,0.006329052,0.1766219],"study_design_scores_gemma":[0.000344326,0.003243213,0.01994823,0.0006178557,0.0006496609,0.00171529,0.001255101,0.6607164,0.2547704,0.0252619,0.03113247,0.0003452361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8332198,0.01067017,0.1261483,0.004089965,0.00279428,0.0003139714,0.001838477,0.003093224,0.01783191],"genre_scores_gemma":[0.9745067,0.001118694,0.02005051,0.0004662735,0.00009313397,0.0001427635,0.0004736978,0.0006293057,0.002518961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005603858,"threshold_uncertainty_score":0.01874673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02662619940739754,"score_gpt":0.2542359264108689,"score_spread":0.2276097270034714,"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."}}