{"id":"W3037971676","doi":"","title":"Scalable Gradients for Stochastic Differential Equations","year":2020,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Stochastic differential equation; Stochastic partial differential equation; Computer science; Applied mathematics; Ordinary differential equation; Differential equation; Mathematics; Scalability; Sensitivity (control systems); Mathematical optimization; Mathematical analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003701502,0.0001172754,0.0001153234,0.00003642112,0.0001279383,0.0001342106,0.000130904,0.00002476824,0.001585283],"category_scores_gemma":[0.00007025038,0.0001127871,0.00003667069,0.00005173912,0.00007463908,0.00006925733,0.0000275895,0.0001084118,0.00006674387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008500246,"about_ca_system_score_gemma":0.00002888604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001497795,"about_ca_topic_score_gemma":0.000003264383,"domain_scores_codex":[0.9991913,0.00001917429,0.0002438723,0.0002373461,0.000158799,0.0001494675],"domain_scores_gemma":[0.9993764,0.0001850069,0.00007526729,0.00005845785,0.0001801568,0.0001247602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001049223,0.00006023329,0.0000144008,0.000002849799,0.00002566642,3.46813e-7,0.0001827836,0.002591059,0.0003850516,0.9047673,0.0005734587,0.0912919],"study_design_scores_gemma":[0.00005143353,0.0001113011,0.00001097054,0.00001068892,0.00001337089,2.693789e-7,0.0002589929,0.8096203,0.0006227221,0.1889951,0.0001920887,0.000112728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002325355,0.000002537374,0.9933671,0.001609781,0.0004988359,0.0001840596,0.0005738411,0.00001859098,0.001419927],"genre_scores_gemma":[0.9964086,0.000005091938,0.002397182,0.0002004015,0.0004801803,0.000035536,0.0001988358,0.000009241055,0.0002648718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9940833,"threshold_uncertainty_score":0.9993274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1583036032922305,"score_gpt":0.3499637908190082,"score_spread":0.1916601875267778,"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."}}