[KongchangAI]
ModelStructured State Space

S4

A Structured State Space Sequence model and an early representative SSM architecture. It applies special structure to matrix A via HiPPO initialization to capture long-range dependencies efficiently, and serves as the foundation for subsequent works such as Mamba.

Timeline (last 90 days)

Oct 28

The experiments focus on discrete groups such as S4 and sparse graphs; extension to larger scales, continuous groups, and dense graphs has not yet been carried out.

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Oct 14

Most SSM and linear attention models use a linear attention mechanism at the bottom layer, with Mamba-2 implemented through the SSD structure and xLSTM implemented through covariance updates.

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Oct 12

Typical representatives of state space models include S4, Mamba and others, which write the recurrence as a continuous differential equation or discrete linear recurrence and achieve efficient parallel training with the help of convolution or selective scan.

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Oct 6

Early SSMs such as S4 efficiently capture long-range dependencies by applying HiPPO initialization to matrix A, while maintaining the inference efficiency of linear recurrence.

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Aug 25

Gu等人在2021年的HiPPO和S4工作中将状态空间模型引入深度学习,开创了结构化状态空间模型研究方向

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