Summary of Implicit Regularization Of Sharpness-aware Minimization For Scale-invariant Problems, by Bingcong Li et al.
Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
by Bingcong Li, Liang Zhang, Niao He
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper explores the implications of Sharpness-Aware Minimization (SAM) for deep learning tasks. Building upon LoRA-inspired architectures, it investigates how SAM regularizes scale-invariant problems involving two groups of variables. The concept of balancedness is introduced, defined as the difference between the squared norms of these variables. This allows for a richer depiction of SAM’s global behaviors. Theoretical and empirical findings reveal that SAM promotes balancedness and that its regularization on balancedness is data-responsive, with outliers having a stronger impact. These observations align with the empirically observed superiority of SAM over Stochastic Gradient Descent (SGD) in the presence of outliers. To leverage this implicit regularization, the paper develops a resource-efficient variant, Balancedness-Aware Regularization (BAR), tailored for scale-invariant problems such as finetuning language models with LoRA. BAR achieves enhanced test performance across various tasks on RoBERTa, GPT2, and OPT-1.3B while saving 95% computational overhead of SAM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAM helps deep learning models generalize better by promoting balancedness in scale-invariant problems. Researchers found that SAM regularizes variables to have equal importance, which is good for handling outliers. This means SAM performs well when data is noisy or has unusual patterns. The paper also shows how to make SAM more efficient without sacrificing performance. By using this technique, models like RoBERTa and GPT2 can work better with less computational power. |
Keywords
» Artificial intelligence » Deep learning » Lora » Regularization » Sam » Stochastic gradient descent
