Summary of The Hidden Attention Of Mamba Models, by Ameen Ali et al.
The Hidden Attention of Mamba Models
by Ameen Ali, Itamar Zimerman, Lior Wolf
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 an efficient selective state space model (SSM) called Mamba, which excels in modeling multiple domains such as natural language processing, long-range sequence processing, and computer vision. The Mamba layer is a dual model that trains in parallel on the entire sequence via an IO-aware parallel scan and deploys autoregressively. By adding a third view, researchers can treat SSMs like attention-driven models, enabling comparisons with self-attention layers in transformers. This new perspective also allows for explainability methods to be applied to the Mamba model, providing insights into its inner workings. The code is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an innovative machine learning model called Mamba that can process data from different fields like language, sequences, and images. This model has three views: training on the whole sequence in parallel, deploying autoregressively, and treating it as an attention-driven model. By comparing these views to transformers, researchers can understand how the Mamba model works better. The code for this model is available for everyone to use. |
Keywords
* Artificial intelligence * Attention * Machine learning * Natural language processing * Self attention
