Summary of Pruning Is Optimal For Learning Sparse Features in High-dimensions, by Nuri Mert Vural and Murat A. Erdogdu
Pruning is Optimal for Learning Sparse Features in High-Dimensions
by Nuri Mert Vural, Murat A. Erdogdu
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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 proposed paper investigates the phenomenon where pruning neural networks to a certain level of sparsity improves feature quality. The authors demonstrate that a broad class of statistical models can be optimally learned using pruned neural networks trained with gradient descent, in high-dimensional spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pruning neural networks to improve feature quality is a common practice, but it’s unclear why this works. This paper explores the idea that certain types of statistical models can be optimized using pruned neural networks and gradient descent, even when dealing with large amounts of data. |
Keywords
» Artificial intelligence » Gradient descent » Pruning
