May 21, 2024, 4:44 a.m. | Fabio Bonassi, Carl Andersson, Per Mattsson, Thomas B. Sch\"on

cs.LG updates on

arXiv:2312.06211v2 Announce Type: replace-cross
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 to reframe SSMs as an extension of a model class commonly used …

abstract arxiv become classification community cs.lg identification introduction machine machine learning paper popular regression replace space ssms state type

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