Computer Science > Machine Learning
arXiv:2410.14067 (cs)
[Submitted on 17 Oct 2024 (v1), last revised 31 Oct 2024 (this version, v2)]
Title:Provable Benefits of Complex Parameterizations for Structured State Space Models
View a PDF of the paper titled Provable Benefits of Complex Parameterizations for Structured State Space Models, by Yuval Ran-Milo and 5 other authors
View PDFHTML (experimental)Abstract:Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. Theoretically explaining the benefits of complex parameterizations for SSMs is an open problem. The current paper takes a step towards its resolution, by establishing formal gaps between real and complex diagonal SSMs. Firstly, we prove that while a moderate dimension suffices in order for a complex SSM to express all mappings of a real SSM, a much higher dimension is needed for a real SSM to express mappings of a complex SSM. Secondly, we prove that even if the dimension of a real SSM is high enough to express a given mapping, typically, doing so requires the parameters of the real SSM to hold exponentially large values, which cannot be learned in practice. In contrast, a complex SSM can express any given mapping with moderate parameter values. Experiments corroborate our theory, and suggest a potential extension of the theory that accounts for selectivity, a new architectural feature yielding state of the art performance.
| Comments: | 12 pages. Accepted to NeurIPS 2024 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2410.14067 [cs.LG] |
| (orarXiv:2410.14067v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2410.14067 arXiv-issued DOI via DataCite |
Submission history
From: Eden Lumbroso [view email][v1] Thu, 17 Oct 2024 22:35:50 UTC (443 KB)
[v2] Thu, 31 Oct 2024 10:38:47 UTC (72 KB)
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View a PDF of the paper titled Provable Benefits of Complex Parameterizations for Structured State Space Models, by Yuval Ran-Milo and 5 other authors
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