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Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4).
Untersuchungen zu dynamischen neuronalen netzen
Sepp Hochreiter · 1991
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré
Cited in the paper.
On the parameterization and initialization of diagonal state space models
Albert Gu, Ankit Gupta, Karan Goel, and Christopher Ré
Cited in the paper.
Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
Later among the works it cites.
Catformer: Designing stable transformers via sensitivity analysis
Jared Quincy Davis, Albert Gu, Tri Dao, Krzysztof Choromanski, Christopher Ré, Percy Liang, and Chelsea Finn · 2021
Later among the works it cites.
Combining recurrent, convolutional, and continuous-time models with the structured learnable linear state space layer
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
Later among the works it cites.
Diagonal state spaces are as effective as structured state spaces
Ankit Gupta · 2022
Closest in time.
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