Summary of Parameter Efficient Mamba Tuning Via Projector-targeted Diagonal-centric Linear Transformation, by Seokil Ham et al.
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
by Seokil Ham, Hee-Seon Kim, Sangmin Woo, Changick Kim
First submitted to arxiv on: 21 Nov 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 study explores parameter-efficient fine-tuning (PEFT) approaches for the Mamba architecture, a potential replacement for Transformer architecture. The authors introduce two key insights-driven strategies: first, they find that Projectors, not state-space models (SSMs), are the primary contributors to transfer learning in Mamba architecture. Second, they propose a novel PEFT method called ProDiaL, which optimizes only diagonal-centric linear transformation matrices, using less than 1% of total parameters. ProDiaL shows strong performance across vision and language Mamba models, highlighting its versatility and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba architecture is a new way to do something with computers that might replace an old method called Transformer architecture. This study tries to make it faster by finding the most important parts to change when learning new things. They found that some special tools called Projectors are super important for making this happen, not another thing called state-space models. They also came up with a new way to do this, called ProDiaL, which only changes certain parts of the computer program and it works really well. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Transfer learning » Transformer
