Summary of Understanding the Generalization Benefits Of Late Learning Rate Decay, by Yinuo Ren et al.
Understanding the Generalization Benefits of Late Learning Rate Decay
by Yinuo Ren, Chao Ma, Lexing Ying
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Neural networks trained with large learning rates for longer periods often exhibit better generalization. This paper investigates this phenomenon by examining the relationship between training and testing losses. Visualization reveals that large learning rates guide models through the minima manifold, eventually approaching the neighborhood of the testing loss minimum. Motivated by these findings, a new nonlinear model is introduced to mimic real neural network loss landscapes. Experimental results show that extended phases with high learning rates steer models towards the minimum norm solution, potentially achieving near-optimal generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Why do some neural networks work better than others? This paper tries to figure out why training neural networks for a long time with big learning rates often leads to better results. By looking at how training and testing losses change during training, they noticed that big learning rates help models find the right spot in “loss land” where they can learn best. They created a new model that acts like real neural networks and tested it by training it for a long time with different learning rates. What they found was that training with big learning rates helps models become really good at generalizing, which is important for making accurate predictions. |
Keywords
* Artificial intelligence * Generalization * Neural network
