Summary of Hyperboliclr: Epoch Insensitive Learning Rate Scheduler, by Tae-geun Kim
HyperbolicLR: Epoch insensitive learning rate scheduler
by Tae-Geun Kim
First submitted to arxiv on: 21 Jul 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 The proposed learning rate schedulers, HyperbolicLR and ExpHyperbolicLR, aim to address the epoch sensitivity problem in conventional methods. By leveraging hyperbolic curves’ asymptotic behavior, these schedulers maintain stable learning curves across varying epoch settings. The paper evaluates performance on various deep learning tasks, including image classification, time series forecasting, and operator learning. Experimental results show that both schedulers achieve more consistent performance improvements than conventional schedulers as training duration grows. This suggests a more robust and efficient approach to deep network optimization, particularly in resource-constrained scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes two new learning rate schedulers to fix the problem of inconsistent learning curves in traditional methods. It uses special math to create stable learning curves that work well even when the number of training steps changes. The schedulers are tested on different tasks like recognizing pictures, predicting future data, and learning how to do operations. The results show that these new schedulers are better than the old ones as they get more consistent results with longer training times. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Optimization » Time series
