{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scalable-variational-inference-for-dynamical","title":"Scalable Variational Inference for Dynamical Systems","arxiv_id":"1705.07079","date":"2017-05-19","proceeding":"NeurIPS 2017 12","authors":["Nico S. Gorbach","Stefan Bauer","Joachim M. Buhmann"],"abstract":"Gradient matching is a promising tool for learning parameters and state\ndynamics of ordinary differential equations. It is a grid free inference\napproach, which, for fully observable systems is at times competitive with\nnumerical integration. However, for many real-world applications, only sparse\nobservations are available or even unobserved variables are included in the\nmodel description. In these cases most gradient matching methods are difficult\nto apply or simply do not provide satisfactory results. That is why, despite\nthe high computational cost, numerical integration is still the gold standard\nin many applications. Using an existing gradient matching approach, we propose\na scalable variational inference framework which can infer states and\nparameters simultaneously, offers computational speedups, improved accuracy and\nworks well even under model misspecifications in a partially observable system.","url_abs":"http://arxiv.org/abs/1705.07079v2","url_pdf":"http://arxiv.org/pdf/1705.07079v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scalable-variational-inference-for-dynamical","repo_url":"https://github.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"numerical-integration","task_name":"Numerical Integration"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.07079","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}