{"id":"W4389491867","doi":"10.1145/3613424.3614248","title":"Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; VMware","keywords":"Computer science; Artificial neural network; Parallel computing; Deep neural networks; Artificial intelligence; Computational science","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":[0.0006398558,0.001584271,0.0005218775,0.0005501817,0.0005937042,0.001345637,0.002515253,0.0007145522,0.01078242],"category_scores_gemma":[0.003080123,0.0006145294,0.0006053835,0.0008577343,0.0008113048,0.001786298,0.001302303,0.001831977,0.002827402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254648,"about_ca_system_score_gemma":0.001975613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008430881,"about_ca_topic_score_gemma":0.01516155,"domain_scores_codex":[0.9993011,0.0001549976,0.00005067931,0.0001581115,0.0002087194,0.0001264278],"domain_scores_gemma":[0.9989957,0.0002944582,0.00004332105,0.0002980528,0.0002550275,0.0001134169],"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":[0.00271868,0.0007026162,0.01001859,0.001082903,0.0003456807,0.001032843,0.0009417497,0.3052539,0.06939343,0.04479805,0.1670423,0.3966693],"study_design_scores_gemma":[0.0002147599,0.0001656693,0.0009397369,0.00003517944,0.00002903839,0.00008100617,0.000144199,0.9297445,0.02582254,0.01753732,0.0252359,0.00005001256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1627132,0.001194577,0.6059983,0.001093054,0.0008656669,0.000337621,0.002462882,0.1964723,0.02886243],"genre_scores_gemma":[0.6385868,0.0004888042,0.3360319,0.0005070876,0.00006077308,0.0003912948,0.005058882,0.009957844,0.008916608],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01078242,"threshold_uncertainty_score":0.03607076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01947132899414691,"score_gpt":0.3088752826142522,"score_spread":0.2894039536201053,"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."}}