{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005706172,0.001302217,0.0006452134,0.0007521284,0.0004145079,0.0008074363,0.00201135,0.0007433171,0.002488014],"category_scores_gemma":[0.001312726,0.0003588618,0.000548571,0.0007387429,0.0005308105,0.001955081,0.001312625,0.001030144,0.0008975894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009327018,"about_ca_system_score_gemma":0.001021713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008158729,"about_ca_topic_score_gemma":0.01498174,"domain_scores_codex":[0.9997967,0.00003139657,0.00001118885,0.00007353009,0.00005052796,0.00003664814],"domain_scores_gemma":[0.9996771,0.0000602038,0.00003911753,0.00009371582,0.00009098516,0.00003907029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007111029,0.0003172546,0.004038739,0.0003119255,0.0003294419,0.0003612589,0.0001174929,0.3935553,0.02845499,0.01421848,0.050156,0.5074281],"study_design_scores_gemma":[0.00004139284,0.0001753098,0.0005474518,0.00001697824,0.00004077392,0.0001131674,0.00002882565,0.9744678,0.01206988,0.003904148,0.008569781,0.00002442033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1664302,0.002169874,0.7982779,0.0009084796,0.0004954675,0.0003638272,0.002499521,0.01388363,0.01497114],"genre_scores_gemma":[0.6460035,0.0008019307,0.3234574,0.0009116639,0.0001117099,0.0002971944,0.01027145,0.0007450256,0.01740009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008158729,"threshold_uncertainty_score":0.01622254,"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."}}