Summary of Cyclical Log Annealing As a Learning Rate Scheduler, by Philip Naveen
Cyclical Log Annealing as a Learning Rate Scheduler
by Philip Naveen
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a novel logarithmic learning rate scheduler for model training, which employs harsh restarting of step sizes using stochastic gradient descent. The Cyclical Log Annealing (CLA) algorithm is designed to allow the use of greedy algorithms on online convex optimization frameworks. In experiments, CLA performed similarly to cosine annealing when used with large transformer-enhanced residual neural networks on the CIFAR-10 image dataset. Future work involves testing the scheduler in generative adversarial networks and optimizing its parameters through further experimentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to adjust how much models learn during training, called Cyclical Log Annealing (CLA). It’s like a recipe for making model updates, where you restart the process sometimes to avoid getting stuck. This helps models work better with big datasets and complex networks. The authors tested CLA on some image recognition tasks and found it did just as well as another popular method. Next, they want to try using CLA with other types of machine learning models and figure out the best way to use it. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Stochastic gradient descent * Transformer
