Summary of Probabilistic Learning Rate Scheduler with Provable Convergence, by Dahlia Devapriya et al.
Probabilistic learning rate scheduler with provable convergence
by Dahlia Devapriya, Thulasi Tholeti, Janani Suresh, Sheetal Kalyani
First submitted to arxiv on: 10 Jul 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 The abstract proposes a probabilistic learning rate scheduler (PLRS) that deviates from traditional monotonically decreasing rates, yet still provides provable convergence guarantees. This addresses the theoretical gap in existing schedulers, which often increase and decrease rates throughout training. The PLRS is tested on various datasets and architectures, showing competitive performance with state-of-the-art schedulers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to control how quickly machines learn from data. Right now, people use schedules that change the learning rate (how fast the machine learns) many times during training. But these schedules haven’t been proven to work well in theory. This new schedule, called PLRS, can be shown mathematically to work well and performs just as well as other top-notch schedules on different datasets and machine architectures. |
