Summary of Incremental Gauss-newton Descent For Machine Learning, by Mikalai Korbit and Mario Zanon
Incremental Gauss-Newton Descent for Machine Learning
by Mikalai Korbit, Mario Zanon
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 presents a new modification of Stochastic Gradient Descent (SGD), called Incremental Gauss-Newton Descent (IGND). IGDN exploits approximate second-order information based on the Gauss-Newton approach to accelerate SGD. The algorithm has the same computational burden as standard SGD but appears to converge faster on certain classes of problems and can be accelerated. The key intuition behind IGDN is that incremental approximate second-order information can be condensed into a scalar value acting as a scaling constant for the update. The paper derives IGDN from Gauss-Newton methods in a general setting, showing it can be interpreted as a well-scaled version of SGD, making tuning simpler and providing increased robustness. Simulations demonstrate IGDN outperforms SGD while performing at least as well in worst-case scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a popular machine learning technique called Stochastic Gradient Descent (SGD). The new method is faster and more efficient than the original. It uses information from previous calculations to make better guesses about how to adjust the algorithm. This helps it learn faster and makes it more robust to mistakes. The researchers tested their new method on various problems and found it outperformed the old one in many cases. |
Keywords
» Artificial intelligence » Machine learning » Stochastic gradient descent
