Summary of Parameter-free Clipped Gradient Descent Meets Polyak, by Yuki Takezawa et al.
Parameter-free Clipped Gradient Descent Meets Polyak
by Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada
First submitted to arxiv on: 23 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 researchers propose a novel algorithm called Inexact Polyak Stepsize for clipped gradient descent that eliminates the need for manual tuning of hyperparameters. This approach converges to the optimal solution without any hyperparameter adjustments and its convergence rate is independent of the loss function’s smoothness parameters. The authors demonstrate the effectiveness of their method using LSTM, Nano-GPT, and T5 models on a synthetic function. Additionally, they investigate the application of parameter-free methods for clipped gradient descent in machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning model training uses algorithms like gradient descent, but these algorithms require careful tuning of hyperparameters. To make this process faster, researchers have developed “parameter-free” methods that adjust hyperparameters automatically. However, most studies have only focused on the stepsize hyperparameter. This study explores a new method called Inexact Polyak Stepsize for clipped gradient descent. It’s like a shortcut to finding the best settings for your model. The authors test this method using different models and a made-up function. |
Keywords
» Artificial intelligence » Gpt » Gradient descent » Hyperparameter » Loss function » Lstm » Machine learning » T5
