{"id":"W4387761026","doi":"10.1109/ipccc59175.2023.10253877","title":"Evaluation of Pruning Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Pruning; Computer science; Memory footprint; Speedup; Inference; Convolutional neural network; Computation; Artificial intelligence; Contextual image classification; Machine learning; Parallel computing; Image (mathematics); Algorithm","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":[0.002734668,0.001568448,0.001197141,0.001647495,0.0009319449,0.001070445,0.002461426,0.001530307,0.002168949],"category_scores_gemma":[0.01645146,0.0003994165,0.0008513372,0.001688254,0.0004671857,0.001826238,0.0008251636,0.00105689,0.0006139826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049849,"about_ca_system_score_gemma":0.001730876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01117528,"about_ca_topic_score_gemma":0.01287435,"domain_scores_codex":[0.9975505,0.0004699221,0.0002435798,0.0004171003,0.001027974,0.0002909358],"domain_scores_gemma":[0.9896773,0.006398178,0.0005741462,0.001046838,0.002122211,0.0001812949],"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":[0.002172623,0.0005763661,0.01096951,0.001656311,0.0005870636,0.0007115247,0.0003340582,0.3493976,0.02682506,0.006469745,0.01427073,0.5860295],"study_design_scores_gemma":[0.0001934151,0.0008727317,0.006131783,0.0002530147,0.0003410807,0.0008014095,0.0002408304,0.9385297,0.04107579,0.002779004,0.00873819,0.00004306053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6911908,0.01512636,0.2597354,0.001015093,0.000737038,0.0006637769,0.002465301,0.009001313,0.02006491],"genre_scores_gemma":[0.6672488,0.004105663,0.3174797,0.0003133065,0.0001221323,0.00028316,0.005106499,0.0009252913,0.004415479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01117528,"threshold_uncertainty_score":0.02222043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08284009450376618,"score_gpt":0.3678937605204616,"score_spread":0.2850536660166954,"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."}}