Summary of Empirical Tests Of Optimization Assumptions in Deep Learning, by Hoang Tran et al.
Empirical Tests of Optimization Assumptions in Deep Learning
by Hoang Tran, Qinzi Zhang, Ashok Cutkosky
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 In this paper, researchers investigate the gap between theoretical understanding and practical performance of deep learning optimization algorithms. They develop new metrics to measure the reliability of assumptions made in theoretical analysis, finding that existing assumptions fail to accurately capture optimization performance. The study highlights the need for empirical verification of analytical assumptions used in theoretical analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well our current theories about how deep learning algorithms work match up with what actually happens when we use them. They create new ways to measure this and find that many of the assumptions we make don’t really reflect reality. This means we need a better way to check if our theories are correct. |
Keywords
* Artificial intelligence * Deep learning * Optimization
