Summary of The Epochal Sawtooth Effect: Unveiling Training Loss Oscillations in Adam and Other Optimizers, by Qi Liu et al.
The Epochal Sawtooth Effect: Unveiling Training Loss Oscillations in Adam and Other Optimizers
by Qi Liu, Wanjing Ma
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: This paper investigates the Epochal Sawtooth Effect (ESE), a recurring pattern observed during training with adaptive gradient-based optimizers like Adam. The ESE is characterized by a sharp loss drop at epoch start, followed by gradual increase, resulting in a sawtooth-shaped loss curve. While most pronounced with Adam, this effect persists albeit less severely with other optimizers like RMSProp. The authors empirically demonstrate this phenomenon and analyze its implications for training deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Low Difficulty summary: This paper looks at something called the Epochal Sawtooth Effect that happens when you’re training a model using certain types of algorithms. It’s like a sawtooth pattern in the way the model performs, with a big improvement at first and then gradually getting worse again. The researchers found out that this effect is most noticeable when using an algorithm called Adam, but it still happens to some extent even with other algorithms. They studied this phenomenon to better understand how models learn. |
