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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2312.06211 |
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| _version_ | 1866929347475013632 |
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| author | Bonassi, Fabio Andersson, Carl Mattsson, Per Schön, Thomas B. |
| author_facet | Bonassi, Fabio Andersson, Carl Mattsson, Per Schön, Thomas B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06211 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Structured state-space models are deep Wiener models Bonassi, Fabio Andersson, Carl Mattsson, Per Schön, Thomas B. Systems and Control Machine Learning 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. |
| title | Structured state-space models are deep Wiener models |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2312.06211 |