Summary of A Survey Of Mamba, by Haohao Qu et al.
A Survey of Mamba
by Haohao Qu, Liangbo Ning, Rui An, Wenqi Fan, Tyler Derr, Hui Liu, Xin Xu, Qing Li
First submitted to arxiv on: 2 Aug 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 The paper explores the Transformer architecture’s limitations and the emergence of the Mamba model as a promising alternative in deep learning. While Transformers have achieved impressive results, they are hindered by quadratic computation complexity, making inference time-consuming. Mamba, inspired by classical state space models, offers comparable modeling abilities to Transformers while preserving near-linear scalability. The paper reviews recent studies on Mamba-empowered models, techniques for adapting Mamba to diverse data, and applications where Mamba excels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new way of building deep learning models that’s faster than old methods like Transformer. Right now, these fast models are only used in special areas like language processing, but people think they could be useful elsewhere too. This paper looks at the latest research on these Mamba models and how they work. |
Keywords
* Artificial intelligence * Deep learning * Inference * Transformer
