Summary of A Method For Enhancing Generalization Of Adam by Multiple Integrations, By Long Jin et al.
A Method for Enhancing Generalization of Adam by Multiple Integrations
by Long Jin, Han Nong, Liangming Chen, Zhenming Su
First submitted to arxiv on: 17 Dec 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 MIAdam, a novel optimizer that integrates multiple integral terms into Adam, is proposed to enhance generalization capability. By filtering out sharp minima during optimization, MIAdam guides the optimizer towards flatter regions, leading to improved generalization. Theoretical explanations and experimental results demonstrate MIAdam’s effectiveness in enhancing robustness against label noise while maintaining rapid convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MIAdam is a new way of optimizing models that helps them generalize better. It does this by smoothing out the bumps in the optimization process, so the model learns more about the underlying patterns rather than just memorizing the training data. This makes MIAdam good at handling noisy labels and improves its performance on state-of-the-art benchmarks. |
Keywords
» Artificial intelligence » Generalization » Optimization
