Summary of Smmf: Square-matricized Momentum Factorization For Memory-efficient Optimization, by Kwangryeol Park and Seulki Lee
SMMF: Square-Matricized Momentum Factorization for Memory-Efficient Optimization
by Kwangryeol Park, Seulki Lee
First submitted to arxiv on: 12 Dec 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 research proposes SMMF (Square-Matricized Momentum Factorization), a novel optimizer that significantly reduces the memory requirements of widely used adaptive learning rate optimizers like Adam. By factorizing momentum tensors with arbitrary rank, SMMF enables efficient optimization for various deep models, including CNNs and Transformers. The proposed approach is based on square-matricization and single matrix factorization, which allows for flexible and efficient factorization of momenta tensors. The authors conduct a regret bound analysis, showing that SMMF converges similarly to non-memory-efficient optimizers like AdamNC, providing theoretical support for its competitive optimization capabilities. Experimental results demonstrate that SMMF uses up to 96% less memory compared to state-of-the-art memory-efficient optimizers while achieving comparable model performance on various CNN and Transformer tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SMMF is a new way to make machine learning models work better with less memory. It’s like a super-powerful optimization tool that helps models learn faster and more efficiently. The authors of this paper came up with SMMF because they wanted to find a way to make existing optimizers, like Adam, use less memory without sacrificing performance. They tested SMMF on different types of models, like CNNs and Transformers, and found that it worked really well, using much less memory than other methods while still getting good results. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Optimization » Transformer
