Summary of Enhanced Structured State Space Models Via Grouped Fir Filtering and Attention Sink Mechanisms, by Tian Meng et al.
Enhanced Structured State Space Models via Grouped FIR Filtering and Attention Sink Mechanisms
by Tian Meng, Yang Tao, Wuliang Yin
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new architecture is proposed that improves upon existing Structured State Space Models (SSSMs) by decomposing matrix multiplications into groups and optimizing positional encoding through Grouped Finite Impulse Response (FIR) filtering. This approach, called Grouped FIR-enhanced SSM (GFSSM), uses semiseparable matrices for efficient computation. Additionally, the authors incorporate a mechanism inspired by the “attention sink” phenomenon to enhance stability and performance over extended sequences. The proposed model aims to bridge the gap between SSMs and Transformer architectures, offering a path forward for scalable and high-performing sequence modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way of building models that process sequences of data. It’s called a Structured State Space Model (SSM). Right now, there are some problems with these models that make them hard to train. The authors of this paper found a solution by breaking down the complicated math into smaller groups and using a special kind of filtering to help the model learn. They also added a new trick inspired by other successful models. This new architecture is called Grouped Finite Impulse Response SSM (GFSSM) and it could help make sequence modeling easier and more powerful. |
Keywords
* Artificial intelligence * Attention * Positional encoding * Transformer
