{"id":"W4416183714","doi":"10.1109/mipr67560.2025.00030","title":"Neural Network Structural Pruning and Acceleration in Frequency Domain","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pruning; Frequency domain; Inference; Acceleration; Discrete cosine transform; Computation; Artificial neural network; Set (abstract data type); Discrete Fourier transform (general); Domain (mathematical analysis)","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":[0.0004776769,0.0009689416,0.0006395619,0.0005852599,0.0003362372,0.0006497509,0.001245636,0.00071963,0.002190551],"category_scores_gemma":[0.002956764,0.0003618678,0.0005148103,0.0005935726,0.0004133543,0.00161446,0.0007379554,0.001564842,0.0005722893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005982949,"about_ca_system_score_gemma":0.001059564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009326599,"about_ca_topic_score_gemma":0.01620972,"domain_scores_codex":[0.9997024,0.00004293896,0.00002060572,0.00006230446,0.0001246418,0.0000470023],"domain_scores_gemma":[0.9992766,0.0003127983,0.00007444541,0.0001675011,0.0001434234,0.0000253111],"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":[0.0002830556,0.0001757741,0.001945889,0.0002024747,0.0001126005,0.0002462291,0.000160623,0.5423083,0.02848199,0.01197438,0.005488625,0.4086201],"study_design_scores_gemma":[0.000008284523,0.00003346728,0.0002342856,0.000006992907,0.00001190496,0.00005391441,0.00001189037,0.9910783,0.004746825,0.002857789,0.0009523279,0.00000394052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1373858,0.00157518,0.8489301,0.0006857043,0.0001608228,0.00007463342,0.0002916727,0.005881662,0.005014423],"genre_scores_gemma":[0.6733084,0.0007382232,0.320322,0.0002533371,0.00007961099,0.0001013764,0.001117233,0.0003509143,0.003728816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009326599,"threshold_uncertainty_score":0.01854461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594840621238933,"score_gpt":0.2816929085465006,"score_spread":0.2657445023341113,"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."}}