SaFARi: State-Space Models for Frame-Agnostic Representation
Abstract
State-Space Models (SSMs) have re-emerged as a powerful tool for online function approximation, and as the backbone of machine learning models for long-range dependent data. However, to date, only a few polynomial bases have been explored for this purpose, and the state-of-the-art implementations were built upon the best of a few limited options. In this paper, we present a generalized method for building an SSM with any frame or basis, rather than being restricted to polynomials. This framework encompasses the approach known as HiPPO, but also permits an infinite diversity of other possible "species" within the SSM architecture. We dub this approach SaFARi: SSMs for Frame-Agnostic Representation.
Cite
Text
Babaei et al. "SaFARi: State-Space Models for Frame-Agnostic Representation." Transactions on Machine Learning Research, 2025.Markdown
[Babaei et al. "SaFARi: State-Space Models for Frame-Agnostic Representation." Transactions on Machine Learning Research, 2025.](https://mlanthology.org/tmlr/2025/babaei2025tmlr-safari/)BibTeX
@article{babaei2025tmlr-safari,
title = {{SaFARi: State-Space Models for Frame-Agnostic Representation}},
author = {Babaei, Hossein and White, Mel and Alemohammad, Sina and Baraniuk, Richard},
journal = {Transactions on Machine Learning Research},
year = {2025},
url = {https://mlanthology.org/tmlr/2025/babaei2025tmlr-safari/}
}