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State Space Models vs RNNs: The Evolution of Sequence Modeling
- The article discusses the evolution of sequence modeling, focusing on State Space Models (SSMs) compared to Recurrent Neural Networks (RNNs).
- SSMs are neural network architectures that incorporate previous SSMs as black box layers, aiming to improve model dimension and state size.
- Various architectures like GSS, Mega, H3, Selective S4, RetNet, and RWKV are introduced, each incorporating unique features like linear attention and efficiency improvements.
- The article emphasizes the importance of state expansion and selective parameters for the performance of SSMs.
- It highlights the connections between RNNs and SSMs, noting that selective SSMs are more powerful due to their parameterizations and initializations.
- Older RNNs faced efficiency and vanishing gradients issues, which were addressed by modern structured SSMs with improved parameterization inspired by classical SSM theory.
- The adoption of discrete analysis and careful parameterization in SSMs has led to more efficient and effective sequence modeling compared to traditional RNNs.
- The article provides insights into the relationship between SSMs, RNNs, and the advancements made in sequence modeling architectures to address efficiency and performance challenges.
- The paper is available on arxiv under the CC BY 4.0 DEED license.
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