Summary of Gradient Span Algorithms Make Predictable Progress in High Dimension, by Felix Benning et al.
Gradient Span Algorithms Make Predictable Progress in High Dimension
by Felix Benning, Leif Döring
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC); Probability (math.PR)
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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 This paper investigates the deterministic behavior of gradient span algorithms on scaled Gaussian random functions as the dimension increases. Specifically, it shows that these algorithms exhibit asymptotically deterministic behavior, which explains why different training runs of large machine learning models result in similar cost curves despite random initialization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how big machine learning models behave when they’re trained multiple times with different starting points. Even though the models are initialized randomly, they tend to produce very similar results. The study proves that certain algorithms, called gradient span algorithms, always follow a predictable pattern on complex landscapes. This finding has important implications for our understanding of large-scale machine learning. |
Keywords
* Artificial intelligence * Machine learning
