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State-Space Models: From S4 to Mamba Reviewed

By PulseAugur Editorial · [1 sources] ·

This paper provides a comprehensive review of Structured State Space Models (SSMs), tracing their evolution from the initial S4 architecture to more advanced models like Mamba and Mamba-2. It analyzes key design dimensions such as input-dependent selectivity, the interplay between recurrent and convolutional views, diagonalization, and caching mechanisms. The review highlights SSMs' efficiency and effectiveness in long-context scenarios, positioning them as competitive alternatives to Transformers, while acknowledging Transformers' strengths in tasks requiring exact retrieval. AI

IMPACT Provides a foundational understanding of SSMs, informing future research and development in efficient sequence modeling.

RANK_REASON The item is a research paper providing a structured review of a class of models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

State-Space Models: From S4 to Mamba Reviewed

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi, Sazzad Bin Bashar Polock, Gaurab Chhetri, Anandi Dutta, Amir Rafe, Subasish Das ·

    Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba

    arXiv:2503.18970v4 Announce Type: replace Abstract: Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient propagation limits of Recurrent Neural Networks (RN…

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