State-Free Inference of State-Space Models: The Transfer Function Approach Article Swipe
Rom Parnichkun
,
Stefano Massaroli
,
Alessandro Moro
,
Jimmy T. H. Smith
,
Ramin Hasani
,
Mathias Lechner
,
Qi An
,
Christopher Ré
,
Hajime Asama
,
Stefano Ermon
,
Taiji Suzuki
,
Atsushi Yamashita
,
Michael Poli
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.06147
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.06147
We approach designing a state-space model for deep learning applications through its dual representation, the transfer function, and uncover a highly efficient sequence parallel inference algorithm that is state-free: unlike other proposed algorithms, state-free inference does not incur any significant memory or computational cost with an increase in state size. We achieve this using properties of the proposed frequency domain transfer function parametrization, which enables direct computation of its corresponding convolutional kernel's spectrum via a single Fast Fourier Transform. Our experimental results across multiple sequence lengths and state sizes illustrates, on average, a 35% training speed improvement over S4 layers -- parametrized in time-domain -- on the Long Range Arena benchmark, while delivering state-of-the-art downstream performances over other attention-free approaches. Moreover, we report improved perplexity in language modeling over a long convolutional Hyena baseline, by simply introducing our transfer function parametrization. Our code is available at https://github.com/ruke1ire/RTF.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.06147
- https://arxiv.org/pdf/2405.06147
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396881944
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https://openalex.org/W4396881944Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.06147Digital Object Identifier
- Title
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State-Free Inference of State-Space Models: The Transfer Function ApproachWork title
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-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-10Full publication date if available
- Authors
-
Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Atsushi Yamashita, Michael PoliList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.06147Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2405.06147Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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-
https://arxiv.org/pdf/2405.06147Direct OA link when available
- Concepts
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Inference, State (computer science), State space, Computer science, Function (biology), Space (punctuation), Mathematical economics, Mathematics, Artificial intelligence, Algorithm, Statistics, Operating system, Evolutionary biology, BiologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.modeling | 127 |
| abstract_inverted_index.multiple | 83 |
| abstract_inverted_index.parallel | 23 |
| abstract_inverted_index.proposed | 31, 57 |
| abstract_inverted_index.sequence | 22, 84 |
| abstract_inverted_index.spectrum | 72 |
| abstract_inverted_index.training | 94 |
| abstract_inverted_index.transfer | 15, 60, 138 |
| abstract_inverted_index.Moreover, | 120 |
| abstract_inverted_index.algorithm | 25 |
| abstract_inverted_index.available | 144 |
| abstract_inverted_index.baseline, | 133 |
| abstract_inverted_index.designing | 2 |
| abstract_inverted_index.efficient | 21 |
| abstract_inverted_index.frequency | 58 |
| abstract_inverted_index.function, | 16 |
| abstract_inverted_index.inference | 24, 34 |
| abstract_inverted_index.Transform. | 78 |
| abstract_inverted_index.benchmark, | 110 |
| abstract_inverted_index.delivering | 112 |
| abstract_inverted_index.downstream | 114 |
| abstract_inverted_index.perplexity | 124 |
| abstract_inverted_index.properties | 54 |
| abstract_inverted_index.state-free | 33 |
| abstract_inverted_index.algorithms, | 32 |
| abstract_inverted_index.approaches. | 119 |
| abstract_inverted_index.computation | 66 |
| abstract_inverted_index.improvement | 96 |
| abstract_inverted_index.introducing | 136 |
| abstract_inverted_index.significant | 39 |
| abstract_inverted_index.state-free: | 28 |
| abstract_inverted_index.state-space | 4 |
| abstract_inverted_index.time-domain | 103 |
| abstract_inverted_index.applications | 9 |
| abstract_inverted_index.experimental | 80 |
| abstract_inverted_index.illustrates, | 89 |
| abstract_inverted_index.parametrized | 101 |
| abstract_inverted_index.performances | 115 |
| abstract_inverted_index.computational | 42 |
| abstract_inverted_index.convolutional | 70, 131 |
| abstract_inverted_index.corresponding | 69 |
| abstract_inverted_index.attention-free | 118 |
| abstract_inverted_index.representation, | 13 |
| abstract_inverted_index.parametrization, | 62 |
| abstract_inverted_index.parametrization. | 140 |
| abstract_inverted_index.state-of-the-art | 113 |
| abstract_inverted_index.https://github.com/ruke1ire/RTF. | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile |