Summary of Improving Generalization and Convergence by Enhancing Implicit Regularization, By Mingze Wang et al.
Improving Generalization and Convergence by Enhancing Implicit Regularization
by Mingze Wang, Jinbo Wang, Haotian He, Zilin Wang, Guanhua Huang, Feiyu Xiong, Zhiyu Li, Weinan E, Lei Wu
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 This research proposes an Implicit Regularization Enhancement (IRE) framework to speed up finding flat solutions in deep learning, leading to improved generalization and convergence. The IRE method decouples sharp and flat directions, reducing sharpness while maintaining training stability. It can be used with generic optimizers without significant computational overhead. The study demonstrates that IRE improves generalization performance on image classification tasks across various datasets (CIFAR-10/100, ImageNet) and models (ResNets, ViTs). Interestingly, IRE also achieves a 2x speed-up compared to AdamW in pre-training Llama models (60M-229M) on datasets including Wikitext-103, Minipile, and Openwebtext. Theoretical guarantees show that IRE accelerates convergence towards flat minima in Sharpness-aware Minimization (SAM). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes deep learning work faster and better! It’s like getting a special tool to help computers learn more quickly and accurately. This tool is called Implicit Regularization Enhancement, or IRE for short. With IRE, computer models can learn from data more efficiently, which means they’ll be better at recognizing pictures and understanding text. The researchers tested IRE on lots of different datasets and showed that it really works! |
Keywords
» Artificial intelligence » Deep learning » Generalization » Image classification » Llama » Regularization » Sam
