Summary of Adam with Model Exponential Moving Average Is Effective For Nonconvex Optimization, by Kwangjun Ahn et al.
Adam with model exponential moving average is effective for nonconvex optimization
by Kwangjun Ahn, Ashok Cutkosky
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 presents a theoretical study on two modern optimization techniques for training large and complex models: adaptive optimization algorithms like Adam, and the model exponential moving average (EMA). The analysis shows that a clipped version of Adam with model EMA achieves optimal convergence rates in various non-convex optimization settings. Additionally, when the scale varies significantly across different coordinates, the coordinate-wise adaptivity of Adam is provably advantageous. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make big models train better. It compares two ways to do this: adaptive algorithms like Adam and a way called exponential moving average (EMA). The study shows that combining these two approaches can help models converge faster in tricky optimization problems. This might be helpful when dealing with very complex models or datasets. |
Keywords
» Artificial intelligence » Optimization
