{"id":"W3126027909","doi":"10.48550/arxiv.2101.06608","title":"Network Automatic Pruning: Start NAP and Take a Nap","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pruning; Nap; Memory footprint; FLOPS; Machine learning; Artificial intelligence; Parallel computing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001600031,0.0003464246,0.0003853254,0.0001102836,0.0003108284,0.0002256466,0.001414298,0.0002230712,0.00002345138],"category_scores_gemma":[0.00002592142,0.0004234127,0.0001194241,0.0009935768,0.0001373521,0.0004373964,0.003237918,0.0006498994,0.00002732139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001353158,"about_ca_system_score_gemma":0.0001849373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002184088,"about_ca_topic_score_gemma":0.00004531129,"domain_scores_codex":[0.9976875,0.000145689,0.000235628,0.001344707,0.0001051697,0.0004813197],"domain_scores_gemma":[0.997535,0.0002099032,0.0002891385,0.0016018,0.0001310994,0.0002330425],"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":[0.000009303376,0.00009464801,0.002002093,0.0001429474,0.000109294,0.0005132073,0.0004502031,0.7817447,0.00003774721,0.2086975,0.001637516,0.00456078],"study_design_scores_gemma":[0.0002617459,0.00003163307,0.002408641,0.0001648269,0.00005837911,0.00002021265,0.00006145378,0.92802,0.00001891637,0.06625129,0.00217869,0.0005242149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2486716,0.0002905602,0.7484908,0.0003256546,0.0003438134,0.000450493,0.000004009449,0.0005045509,0.0009185391],"genre_scores_gemma":[0.9647396,0.0002325806,0.03359403,0.0003126329,0.0001264096,0.00000573459,0.00002000553,0.00002497508,0.0009440714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7160679,"threshold_uncertainty_score":0.9998218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07025394555470299,"score_gpt":0.1945067949380338,"score_spread":0.1242528493833308,"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."}}