{"id":"W2900763636","doi":"10.1109/cvpr.2019.00932","title":"Structured Pruning of Neural Networks With Budget-Aware Regularization","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs; Compute Canada","keywords":"Computer science; Pruning; Regularization (linguistics); Artificial neural network; Inference; Deep neural networks; Artificial intelligence; Residual; Machine learning; Feature (linguistics); Dropout (neural networks); Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001170649,0.001021239,0.0008515919,0.0005785239,0.0003808721,0.000655279,0.00144447,0.001154011,0.002083298],"category_scores_gemma":[0.004930356,0.0004969247,0.000666158,0.0004557625,0.0007794581,0.001344262,0.001152247,0.001568915,0.00056861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007284723,"about_ca_system_score_gemma":0.00090974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002335963,"about_ca_topic_score_gemma":0.005998552,"domain_scores_codex":[0.9994429,0.0001566354,0.00003877757,0.0001105344,0.0001779093,0.00007337359],"domain_scores_gemma":[0.9987528,0.00058157,0.0001399766,0.0002885374,0.0001828918,0.00005419135],"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":[0.0002989645,0.0001301467,0.001461504,0.000232359,0.0001407296,0.0002888524,0.0001542644,0.7299608,0.02555943,0.03018951,0.006258919,0.2053245],"study_design_scores_gemma":[0.00001575781,0.00003564973,0.0001932437,0.00002197129,0.00001605303,0.00005877659,0.000008000864,0.9854837,0.00484663,0.008215575,0.001098713,0.000005895046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04318513,0.0007663615,0.9511746,0.0002700076,0.00006467704,0.00006820485,0.0001054305,0.001278062,0.003087493],"genre_scores_gemma":[0.6089942,0.0005416818,0.381969,0.0004004852,0.00009517476,0.0002406395,0.0005290614,0.000561345,0.006668407],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002335963,"threshold_uncertainty_score":0.006969333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120915455717737,"score_gpt":0.2397131926062216,"score_spread":0.2285040380490442,"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."}}