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A Simplified State Space Model Architecture
- The paper discusses a simplified architecture for selective state space models (SSMs).
- Selective SSMs are standalone sequence transformations that can be incorporated into neural networks.
- The architecture combines the linear attention block and MLP block into one homogeneous stack.
- This simplified architecture is inspired by the gated attention unit (GAU) approach.
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