{"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/neural-ode-processes-a-short-summary","title":"Neural ODE Processes: A Short Summary","arxiv_id":null,"date":"2021-09-27","proceeding":"NeurIPS Workshop DLDE 2021 12","authors":["Alexander Luke Ian Norcliffe","Cristian Bodnar","Ben Day","Jacob Moss","Pietro Lio"],"abstract":"Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series,  NODEs present a few disadvantages.  First, they are unable to adapt to incoming data-points, a fundamental requirement for real-time applications imposed by the natural direction of time.  Second, time-series are often composed of a sparse set of measurements, which could be explained by many possible underlying dynamics.  NODEs do not capture this uncertainty.  To this end, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.  By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points.  At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits. Code is available online at https://github.com/crisbodnar/ndp.","url_abs":"https://openreview.net/forum?id=6yovcKE2LeN","url_pdf":"https://openreview.net/pdf?id=6yovcKE2LeN","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":"neural-ode-processes-a-short-summary","repo_url":"https://github.com/crisbodnar/ndp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}