From system identification to sequence models - a primer on Structured State-Space Models
Some useful resources
Welcome to the this talk at Reglermöte 2025! Below you can find information about the talk, links to github repositories, and the list of publications mentioned during the presentation.
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the machine learning community since, owing to their parallelizability, they can be efficiently and scalably trained to tackle extremely-long sequence classification and regression problems. Interestingly, SSMs appear as an effective way to learn deep Wiener models, which allows to reframe SSMs as an extension of a model class commonly used in system identification. In order to stimulate a fruitful exchange of ideas between the machine learning and system identification communities, we deem it useful to summarize the recent contributions on the topic in a structured and accessible form. At last, we highlight future research directions for which this community could provide impactful contributions.
@inproceedings{bonassi2024structured,title={Structured state-space models are deep Wiener models},year={2024},booktitle={20th IFAC Symposium on System Identification (SYSID)},author={Bonassi, Fabio and Andersson, Carl and Mattsson, Per and Sch{\"o}n, Thomas B},url={https://www.sciencedirect.com/science/article/pii/S2405896324013168},doi={10.1016/j.ifacol.2024.08.536},}