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About this paper
Ankit Gupta, Albert Gu, Jonathan Berant
Diagonal State Space (DSS) models are introduced as a simplified alternative to complex Structured State Space (S4) architectures for efficient long-range sequence modeling. DSS achieves an average accuracy of 81.88% on the Long Range Arena (LRA) benchmark, comparable to S4's 80.21%, by leveraging a diagonal parameterization of state matrices while maintaining the computational efficiency of state space models.
Key insights
- Diagonal state spaces are theoretically proven to be as expressive as general state spaces under specific conditions on eigenvalues, justifying the simplification of the state matrix.
- Careful initialization, specifically using a 'Skew-Hippo initialization' derived from HiPPO theory, is critical for the robust performance of DSS, consistent with findings for S4.
- The ability of state space models to capture long-range dependencies via their convolutional kernels is a primary factor in their superior performance over Transformers on tasks requiring extensive context.
Summary by alphaXiv.
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