Summary of Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers, by Sukjun Hwang et al.
Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers
by Sukjun Hwang, Aakash Lahoti, Tri Dao, Albert Gu
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents a unifying framework for sequence models based on the Transformer architecture. The proposed matrix mixer view encompasses various well-known sequence models, including Transformers, structured state space models (SSSMs), and others. The authors identify a key axis of matrix parameterizations, termed sequence alignment, which increases the flexibility and performance of matrix mixers. This framework allows for developing new sequence mixers with desired properties, leading to several sub-quadratic sequence model proposals. One such proposal is Hydra, a bidirectional extension of the Mamba model, which outperforms BERT on GLUE by 0.8 points and ViT on ImageNet by 2% Top-1 accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how different types of sequence models work. It shows that many popular models are connected by a common framework called the matrix mixer view. This framework allows us to design new models with specific properties, making them more efficient and effective. One example is Hydra, which combines the strengths of previous models to achieve better performance on certain tasks. In particular, it outperforms other models on language understanding and image recognition benchmarks. |
Keywords
» Artificial intelligence » Alignment » Bert » Language understanding » Sequence model » Transformer » Vit
