{"id":"W3125121244","doi":"10.21314/jcf.2016.209","title":"The efficient application of automatic differentiation for computing gradients in financial applications","year":2016,"lang":"en","type":"article","venue":"The Journal of Computational Finance","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computational finance; Automatic differentiation; Speedup; Computation; Mode (computer interface); Function (biology); Monte Carlo method; Algorithm; Finance; Parallel computing; Mathematics","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":[0.0009268466,0.0008404007,0.000601111,0.000869722,0.0004801315,0.0009984242,0.0008386396,0.0006621378,0.00375455],"category_scores_gemma":[0.007015608,0.0003519508,0.0004373742,0.0008896904,0.0008067741,0.001395639,0.0012781,0.001311286,0.001567576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006211954,"about_ca_system_score_gemma":0.001218496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003656937,"about_ca_topic_score_gemma":0.004772373,"domain_scores_codex":[0.9996204,0.0001346622,0.00002963264,0.00004320151,0.000130698,0.00004131109],"domain_scores_gemma":[0.9983676,0.001031893,0.00009172882,0.0002003377,0.0002533528,0.00005502918],"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":[0.0002854246,0.0001382471,0.002499567,0.0003945668,0.000058767,0.0003158139,0.0002509239,0.3500045,0.02072756,0.1702002,0.006877094,0.4482473],"study_design_scores_gemma":[0.00001592725,0.0000245735,0.0001875621,0.00001449676,0.000004874483,0.00005921251,0.00001397086,0.9562768,0.003719669,0.03765904,0.00201508,0.000008856548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01952152,0.0005722992,0.9735241,0.0003167001,0.00009107931,0.00004526672,0.00004702028,0.001032816,0.004849223],"genre_scores_gemma":[0.3474285,0.0005967335,0.6474372,0.0001854143,0.00007360938,0.00009362878,0.0001423368,0.0004274507,0.003615182],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00375455,"threshold_uncertainty_score":0.01256025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00798150592505802,"score_gpt":0.2487353012457836,"score_spread":0.2407537953207255,"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."}}