Structured state-space models are deep Wiener models
Fabio Bonassi Carl Andersson Per Mattsson Thomas B. Schön
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.