{"id":"W2972528742","doi":"10.1007/978-3-030-30484-3_21","title":"Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Convolutional neural network; Pruning; Artificial intelligence; Benchmark (surveying); Modularity (biology); Bridge (graph theory); Deep learning; Network structure; Pattern recognition (psychology); Machine learning","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":[0.0004335718,0.0009193027,0.0005996362,0.0002759321,0.0002168691,0.0007929996,0.00100732,0.0009554384,0.002339673],"category_scores_gemma":[0.002200646,0.0006614474,0.0004583367,0.0004448398,0.0006475883,0.001883901,0.001586991,0.001984438,0.0007150413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005200372,"about_ca_system_score_gemma":0.0006559382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002446974,"about_ca_topic_score_gemma":0.00516133,"domain_scores_codex":[0.9998621,0.0000227438,0.000007007857,0.0000384116,0.00004292056,0.00002683636],"domain_scores_gemma":[0.9994838,0.0002092505,0.00005380227,0.0001148675,0.00009205075,0.00004605951],"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":[0.0001752707,0.00009039803,0.001492933,0.0001695399,0.0000881208,0.0001183482,0.00009181719,0.5748121,0.02275225,0.06230861,0.008682242,0.3292184],"study_design_scores_gemma":[0.000004117944,0.00002316243,0.0001213926,0.00000800122,0.000006741931,0.00001577609,0.000004778853,0.9781901,0.001706004,0.01935619,0.0005594465,0.000004207004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04039083,0.0005034618,0.9539838,0.0003687171,0.00009248214,0.00002451835,0.0002243608,0.0008255761,0.003586225],"genre_scores_gemma":[0.655567,0.0009920407,0.3239853,0.000392336,0.0002128404,0.0001062049,0.001748114,0.0003388773,0.01665732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002446974,"threshold_uncertainty_score":0.007826924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566753841014424,"score_gpt":0.249089511902544,"score_spread":0.2334219734923998,"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."}}