{"id":"W4379528655","doi":"10.1109/tcyb.2023.3278110","title":"Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Pyramid (geometry); Computer science; Artificial intelligence; Block (permutation group theory); Channel (broadcasting); Feature (linguistics); Data mining; Deep learning; Representation (politics); Scale (ratio); Feature extraction; Convolution (computer science); Machine learning; Artificial neural network; Pattern recognition (psychology)","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":[0.001167004,0.0009788787,0.0007353143,0.0003290219,0.0003582653,0.0008345259,0.001377061,0.0008787614,0.001187686],"category_scores_gemma":[0.002322451,0.0005484659,0.0006265822,0.0004590582,0.0009893078,0.001921816,0.001237122,0.001905774,0.0002155754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162383,"about_ca_system_score_gemma":0.001561311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004775211,"about_ca_topic_score_gemma":0.00628798,"domain_scores_codex":[0.9996719,0.00007955741,0.00001701494,0.00009694345,0.00007849358,0.00005595657],"domain_scores_gemma":[0.9994462,0.0003032459,0.00006191573,0.00007288461,0.00008005616,0.00003557726],"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":[0.00003255052,0.00003133471,0.000450792,0.00003121457,0.00002672216,0.00003553255,0.00003371567,0.9537651,0.002562204,0.01155995,0.0005675752,0.0309033],"study_design_scores_gemma":[0.000002122895,0.000007119857,0.0000247694,0.000001268863,0.000003143767,0.000003060347,0.000002212771,0.9965586,0.0004986134,0.002793823,0.0001036617,0.000001541753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02831673,0.0002407375,0.9694365,0.0003161695,0.00001974944,0.00003395893,0.00003745581,0.000372123,0.001226589],"genre_scores_gemma":[0.8052752,0.0002844626,0.1918873,0.0002759068,0.00004154489,0.0001363134,0.0001216733,0.00009389746,0.001883597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004775211,"threshold_uncertainty_score":0.009494781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607447482670018,"score_gpt":0.2989788397853624,"score_spread":0.2629043649586622,"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."}}