Summary of How Does Gradient Descent Learn Features — a Local Analysis For Regularized Two-layer Neural Networks, by Mo Zhou et al.
How Does Gradient Descent Learn Features – A Local Analysis for Regularized Two-Layer Neural Networks
by Mo Zhou, Rong Ge
First submitted to arxiv on: 3 Jun 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 The paper explores another mechanism for neural networks to learn features via gradient descent through a local convergence analysis. By carefully regularizing the objective and analyzing the early stages of gradient-based training, it is shown that feature learning occurs not only initially but also towards the end of training. The authors demonstrate this capability by capturing ground-truth directions once the loss reaches a certain threshold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper investigates how neural networks learn features using a new approach. It shows that when we carefully train the network and analyze its early stages, it can learn important information about the data even at the end of the training process. This is important because many previous studies have shown that neural networks don’t always learn new things as they’re trained. |
Keywords
» Artificial intelligence » Gradient descent
