{"id":"W4224985732","doi":"10.18280/mmep.090221","title":"Using Dynamic Pruning Technique for Efficient Depth Estimation for Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pruning; Computer science; Inference; Monocular; Reduction (mathematics); Prioritization; Machine learning; Artificial intelligence; Encoder; Performance improvement; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003261738,0.0006386884,0.0005636205,0.0005591401,0.0003312746,0.0005191599,0.001171817,0.0006386653,0.001292115],"category_scores_gemma":[0.001163887,0.0004155354,0.0003727771,0.0003928661,0.0003080501,0.0009207144,0.0007686276,0.0008111611,0.0004483295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254768,"about_ca_system_score_gemma":0.000942286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01210885,"about_ca_topic_score_gemma":0.01965962,"domain_scores_codex":[0.9998012,0.00002207076,0.00000793482,0.00005082105,0.00008007437,0.00003781666],"domain_scores_gemma":[0.9997665,0.00008697808,0.00002511647,0.00003619919,0.00007189889,0.00001327297],"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":[0.0002021999,0.00006859405,0.001814582,0.0001048512,0.00008023963,0.0002315409,0.0001549179,0.4082991,0.04463673,0.006424407,0.003773229,0.5342096],"study_design_scores_gemma":[0.000006658103,0.00003033643,0.0003827059,0.00001097561,0.00001307844,0.00006334883,0.00001858076,0.9884448,0.007468846,0.002342021,0.001212707,0.0000059147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04607347,0.0007040806,0.9491531,0.0001792553,0.00006039238,0.00003356153,0.0001361746,0.001684715,0.001975235],"genre_scores_gemma":[0.6861515,0.0006710821,0.3087721,0.0001637646,0.00006131593,0.00006747486,0.000527893,0.0001875876,0.003397183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01210885,"threshold_uncertainty_score":0.02407676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03506975834006713,"score_gpt":0.2753685181674765,"score_spread":0.2402987598274094,"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."}}