Summary of Exact Gauss-newton Optimization For Training Deep Neural Networks, by Mikalai Korbit et al.
Exact Gauss-Newton Optimization for Training Deep Neural Networks
by Mikalai Korbit, Adeyemi D. Adeoye, Alberto Bemporad, Mario Zanon
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 EGN is a new optimization algorithm for machine learning problems with large neural networks. It uses a combination of mathematical techniques to compute the direction in which to update parameters, which can be especially helpful when dealing with very large datasets. The algorithm combines ideas from linear algebra and optimization methods to achieve this. Additionally, it can be modified to include extra features like line search, regularization, and momentum to make it even more effective. Experimental results show that EGN outperforms or matches other popular optimizers in various machine learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EGN is a new way to solve big machine learning problems. It helps us update the weights of our neural networks by finding the best direction to move them. This can be really helpful when we’re dealing with huge amounts of data. The algorithm uses some fancy math to do this, and it’s especially good at solving these kinds of problems. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Regularization
