Summary of Compressible Dynamics in Deep Overparameterized Low-rank Learning & Adaptation, by Can Yaras et al.
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
by Can Yaras, Peng Wang, Laura Balzano, Qing Qu
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Machine Learning (stat.ML)
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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 proposed approach leverages the inherent low-dimensional structures of data and compressible dynamics within model parameters to reap the benefits of overparameterization without computational burdens. In practice, it is demonstrated for deep low-rank matrix completion as well as fine-tuning language models. Theoretical findings ground this approach in deep overparameterized low-rank matrix recovery, where learning dynamics are confined to an invariant low-dimensional subspace. Compact factorizations possessing the same benefits as overparameterized counterparts can be constructed and trained. This technique improves training efficiency while retaining advantages for deep matrix completion. For language model fine-tuning, a method called “Deep LoRA” is proposed, improving existing low-rank adaptation (LoRA) technique to reduce overfitting and simplify hyperparameter setup while maintaining efficiency. Deep LoRA’s effectiveness is validated on natural language tasks, particularly with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make machine learning models more efficient without sacrificing their power. This is done by finding the underlying patterns in data that allow models to be smaller and faster without losing accuracy. The approach is tested for two different applications: filling in missing information in matrices and fine-tuning language models. The results show that this method can significantly improve training speed while keeping the benefits of more complex models. |
Keywords
» Artificial intelligence » Fine tuning » Hyperparameter » Language model » Lora » Low rank adaptation » Machine learning » Overfitting
