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State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture.
Neural nets and the brain-model problem
Minsky, M · 1954
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Algebraic theory of machines. i. prime decomposition theorem for finite semigroups and machines
Krohn, K. and Rhodes, J · 1965
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File change semantics and the familiarity theory of definiteness
Heim, I · 1983
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Bounded-width polynomial-size branching programs recognize exactly those languages in nc1
Barrington, D. A · 1989
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The complexity of iterated multiplication
Immerman, N. and Landau, S · 1989
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Prefix sums and their applications
Blelloch, G. E · 1990
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On threshold circuits and polynomial computation
Reif, J. H. and Tate, S. R · 1992
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Threshold circuits for iterated matrix product and powering
Mereghetti, C. and Palano, B · 2000
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Division is in uniform T C 0 TC^{0}
Hesse, W · 2001
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Introduction to automata theory, languages, and computation
Hopcroft, J. E., Motwani, R., and Ullman, J. D · 2001
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Uniform constant-depth threshold circuits for division and iterated multiplication
Hesse, W., Allender, E., and Barrington, D. A. M · 2002
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Weighted Automata Algorithms , pp. 213–254
Mohri, M · 2009
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Sequential neural networks as automata
Merrill, W · 2019
Cited alongside, same era.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Gu, A., Johnson, I., Goel, K., Saab, K. K., Dao, T., Rudra, A., and Re, C · 2021
Cited alongside, same era.
Chess as a testbed for language model state tracking
Toshniwal, S., Wiseman, S., Livescu, K., and Gimpel, K · 2021
Cited alongside, same era.
Formal language recognition by hard attention transformers: Perspectives from circuit complexity
Hao, S., Angluin, D., and Frank, R · 2022
Hungry hungry hippos: Towards language modeling with state space models
Fu, D. Y., Dao, T., Saab, K. K., Thomas, A. W., Rudra, A., and Re, C · 2023
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Mamba: Linear-time sequence modeling with selective state spaces, 2023
Gu, A. and Dao, T · 2023
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Liquid structural state-space models
Hasani, R., Lechner, M., Wang, T.-H., Chahine, M., Amini, A., and Rus, D · 2023
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Entity tracking in language models
Kim, N. and Schuster, S · 2023
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Transformers learn shortcuts to automata
Liu, B., Ash, J. T., Goel, S., Krishnamurthy, A., and Zhang, C · 2023
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Structured state spaces: Combining continuous-time, recurrent, and convolutional models, January 2022b
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Cited alongside, same era.
The annotated S4
Rush, S. and Karamcheti, S · 2022
Cited alongside, same era.
Masked hard-attention transformers and Boolean RASP recognize exactly the star-free languages, 2023
Angluin, D., Chiang, D., and Yang, A · 2023
Cited alongside, same era.
Tighter bounds on the expressivity of transformer encoders
Chiang, D., Cholak, P., and Pillay, A · 2023
Cited alongside, same era.
Towards revealing the mystery behind chain of thought: A theoretical perspective
Feng, G., Zhang, B., Gu, Y., Ye, H., He, D., and Wang, L · 2023
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Re, C
Cited in the paper.
The parallelism tradeoff: Limitations of log-precision transformers
Merrill, W. and Sabharwal, A
Cited in the paper.
Gu, A., Goel, K., Saab, K., and Ré, C · 2024
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The expressive power of transformers with chain of thought
Merrill, W. and Sabharwal, A · 2024
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What formal languages can transformers express? A survey
Strobl, L., Merrill, W., Weiss, G., Chiang, D., and Angluin, D · 2024
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Mambabyte: Token-free selective state space model, 2024
Wang, J., Gangavarapu, T., Yan, J. N., and Rush, A. M · 2024
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