S7: Selective and Simplified State Space Layers for Sequence Modeling

Understand

A central challenge in sequence modeling is efficiently handling tasks with extended contexts.

• While recent state-space models (SSMs) have made significant progress in this area, they often lack input-dependent filtering or require substantial increases in model complexity to handle input variability.
• We address this gap by introducing S7, a simplified yet powerful SSM that can handle input dependence while incorporating stable reparameterization and specific design choices to dynamically adjust state transitions based on input content, maintaining efficiency and performance.
• We prove that this reparameterization ensures stability in long-sequence modeling by keeping state transitions well-behaved over time.