{"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/learning-latent-fractional-dynamics-with","title":"Learning Latent Fractional dynamics with Unknown Unknowns","arxiv_id":"1811.00703","date":"2018-11-02","proceeding":null,"authors":["Gaurav Gupta","Sergio Pequito","Paul Bogdan"],"abstract":"Despite significant effort in understanding complex systems (CS), we lack a\ntheory for modeling, inference, analysis and efficient control of time-varying\ncomplex networks (TVCNs) in uncertain environments. From brain activity\ndynamics to microbiome, and even chromatin interactions within the genome\narchitecture, many such TVCNs exhibits a pronounced spatio-temporal fractality.\nMoreover, for many TVCNs only limited information (e.g., few variables) is\naccessible for modeling, which hampers the capabilities of analytical tools to\nuncover the true degrees of freedom and infer the CS model, the hidden states\nand their parameters. Another fundamental limitation is that of understanding\nand unveiling of unknown drivers of the dynamics that could sporadically excite\nthe network in ways that straightforward modeling does not work due to our\ninability to model non-stationary processes. Towards addressing these\nchallenges, in this paper, we consider the problem of learning the fractional\ndynamical complex networks under unknown unknowns (i.e., hidden drivers) and\npartial observability (i.e., only partial data is available). More precisely,\nwe consider a generalized modeling approach of TVCNs consisting of\ndiscrete-time fractional dynamical equations and propose an iterative framework\nto determine the network parameterization and predict the state of the system.\nWe showcase the performance of the proposed framework in the context of task\nclassification using real electroencephalogram data.","url_abs":"http://arxiv.org/abs/1811.00703v2","url_pdf":"http://arxiv.org/pdf/1811.00703v2.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":"learning-latent-fractional-dynamics-with","repo_url":"https://github.com/gaurav71531/hiddenState","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}