{"id":"W4401454822","doi":"10.1145/3643794.3648288","title":"MobileNetV3 Layer Sensitivity and Sparsity","year":2024,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Sensitivity (control systems); Layer (electronics); Computer science; Materials science; Engineering; Composite material; Electronic engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002839036,0.00006598487,0.00006671124,0.00004367665,0.00007430839,0.0002514628,0.00009443782,0.00002798519,0.000002489821],"category_scores_gemma":[0.000006618784,0.00005447554,0.00002409633,0.0001566178,0.00001772239,0.0002441579,0.0003069226,0.000080146,0.0001063077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112329,"about_ca_system_score_gemma":0.00002171008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004907596,"about_ca_topic_score_gemma":0.000005261159,"domain_scores_codex":[0.9994074,0.00002918999,0.00006030167,0.0002604791,0.00008420311,0.000158399],"domain_scores_gemma":[0.9996554,0.0001027497,0.000006193955,0.0001679312,0.00001563917,0.00005206643],"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":[0.000002656006,0.00005094671,0.006338993,0.0001124966,0.00004879052,0.00059866,0.00231964,0.00005973742,0.009334344,0.06819926,0.1093774,0.803557],"study_design_scores_gemma":[0.00007203047,0.00003137239,0.01248082,0.0000351372,0.000005015511,0.0001357385,0.000006239376,0.8944563,0.007512756,0.003416981,0.08161616,0.0002314724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3838398,0.0003380105,0.5926811,0.001073558,0.006115053,0.00006061654,8.460737e-8,0.0006259393,0.01526589],"genre_scores_gemma":[0.9875256,0.000006155407,0.01076559,0.0002373276,0.0006516142,4.92688e-7,2.177447e-7,0.000003415467,0.0008096276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8943965,"threshold_uncertainty_score":0.2424861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01812781388081968,"score_gpt":0.2374836723772979,"score_spread":0.2193558584964782,"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."}}