Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

The article discusses the importance of sequence modeling in various domains and the shift from Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) to transformers due to their superior performance. However, transformers have issues with attention complexity and inductive bias handling. To address these, variations using spectral networks or convolutions have been proposed, but they struggle with long sequences. The article suggests State Space Models (SSMs) as a promising alternative, especially with the advent of S4 and its variants.

The article categorizes foundational SSMs into three paradigms: Gating architectures, Structural architectures, and Recurrent architectures. It highlights the diverse applications of SSMs across various domains and consolidates their performance on benchmark datasets. The article also mentions the project page for the Mamba-360 work.

Key takeaways:

Sequence modeling is a critical area in various domains, and while RNNs and LSTMs have historically dominated, transformers have shown superior performance despite their complexity and inductive bias challenges.
State Space Models (SSMs) have emerged as promising alternatives for sequence modeling, especially with the advent of S4 and its variants.
The survey categorizes foundational SSMs based on three paradigms: Gating architectures, Structural architectures, and Recurrent architectures.
SSMs have diverse applications across domains such as vision, video, audio, speech, language, medical, chemical, recommendation systems, and time series analysis, and have shown good performance on various benchmark datasets.

Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

Key takeaways:

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