Generalization Error Analysis for Selective State-Space Models Through the Lens of Attention
PositiveArtificial Intelligence
This paper explores the promising potential of state-space models as an alternative to Transformers for sequence modeling. It provides a theoretical analysis of selective state-space models, particularly focusing on the Mamba model, and introduces a new generalization bound that enhances our understanding of these models.
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