{"id":"W3003482062","doi":"10.3390/app10030884","title":"DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Convolutional neural network; Benchmark (surveying); Computer science; Sigmoid function; MNIST database; Routing (electronic design automation); Artificial intelligence; Network architecture; Pattern recognition (psychology); Field (mathematics); Data mining; Deep learning; Artificial neural network; Computer network; Mathematics; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002193332,0.0001861691,0.0001774019,0.00002998555,0.0007262271,0.000212437,0.001459433,0.00003862687,0.00001652112],"category_scores_gemma":[0.00001581697,0.0001501595,0.00003569982,0.001842106,0.000361247,0.0004022976,0.0003613407,0.0001756377,0.000100223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004441223,"about_ca_system_score_gemma":0.0001096215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007384779,"about_ca_topic_score_gemma":0.00002055385,"domain_scores_codex":[0.9979986,0.00002688518,0.0001882879,0.0007714765,0.0003711178,0.0006436578],"domain_scores_gemma":[0.9991536,0.0000956415,0.0001420206,0.0003476741,0.00002827787,0.0002327967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002986085,0.00006573919,0.0004091545,0.00001481566,0.00002724818,0.00001989876,0.006910662,0.3765468,0.07756136,0.4674947,0.001098625,0.06982118],"study_design_scores_gemma":[0.0007047668,0.0002949108,0.001425836,0.00003579884,0.00002191214,0.000017584,0.0013916,0.9715477,0.01029643,0.01176202,0.001653265,0.0008482086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06985878,0.00004913575,0.9185534,0.002013015,0.0000577805,0.0003399779,0.00000135747,0.0003522455,0.008774279],"genre_scores_gemma":[0.8613055,0.00001033773,0.1364598,0.002021479,0.00009831312,0.00006438357,0.000001315177,0.000008951077,0.00002983125],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7914467,"threshold_uncertainty_score":0.6123329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809426212793714,"score_gpt":0.246648746065005,"score_spread":0.2285544839370679,"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."}}