Structured state-space models are deep Wiener models

Fabio Bonassi, Carl Andersson, Per Mattsson, Thomas B. Schön

Abstract: 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 us to reframe SSMs as an extension of a model class commonly used in system Identification. 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.