{"id":"W2168148636","doi":"","title":"Incorporating Second-Order Functional Knowledge for Better Option Pricing","year":2000,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":273,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Generalization; Computer science; A priori and a posteriori; Class (philosophy); Task (project management); Function (biology); Order (exchange); Convex function; Artificial neural network; Machine learning; Artificial intelligence; Regular polygon; Mathematical optimization; 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":[0.00274523,0.0009902281,0.001225434,0.0005671997,0.000311504,0.001544973,0.0009338008,0.002180309,0.003656572],"category_scores_gemma":[0.008553853,0.000562892,0.0008824836,0.000589206,0.0006535857,0.004974237,0.001139711,0.00303132,0.001130264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006246938,"about_ca_system_score_gemma":0.0009563144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001961522,"about_ca_topic_score_gemma":0.003593667,"domain_scores_codex":[0.9994941,0.000169261,0.00003574916,0.0001014736,0.0001381412,0.00006128831],"domain_scores_gemma":[0.9968342,0.001607283,0.0002515028,0.0008843112,0.0003424593,0.00008023226],"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":[0.0001689462,0.0003899851,0.002248135,0.0001271465,0.0001349474,0.00009044538,0.00008569635,0.7358449,0.01828142,0.02746446,0.001798546,0.2133653],"study_design_scores_gemma":[0.0000094598,0.00002818288,0.0002942579,0.000007653382,0.000007686217,0.00001643474,0.000003302202,0.9901528,0.001692204,0.007283091,0.0004971481,0.00000776209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0418994,0.0004006881,0.9539743,0.0004704314,0.00004902459,0.00002694033,0.00003461886,0.0008457358,0.002298855],"genre_scores_gemma":[0.7320511,0.0005912946,0.2624489,0.0004043712,0.0001254363,0.00007718663,0.0002186017,0.0002100337,0.00387307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003656572,"threshold_uncertainty_score":0.01451832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02175691798115745,"score_gpt":0.2499960200512804,"score_spread":0.228239102070123,"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."}}