Most prior State Space Models (SSMs) use complex numbers to optimize performance, but we find real-valued SSMs outperform them in some contexts, suggesting a continuous-discrete spectrum influence.
We propose using a simplified architecture for Selective State Space Models, emphasizing efficient implementation and demonstrating its effectiveness through empirical evaluations across various tasks, including language and DNA modeling.
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