Summary of Explicit and Implicit Graduated Optimization in Deep Neural Networks, by Naoki Sato et al.
Explicit and Implicit Graduated Optimization in Deep Neural Networks
by Naoki Sato, Hideaki Iiduka
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The medium difficulty summary: This paper investigates the performance of a global optimization technique called graduated optimization, which minimizes nonconvex functions by adding noise and gradually refining the solution. The authors experimentally evaluate an explicit algorithm with optimal noise scheduling and discuss its limitations using traditional benchmark functions and empirical loss functions for modern neural networks. Additionally, the paper extends the implicit graduated optimization algorithm to stochastic gradient descent with momentum, analyzing its convergence and demonstrating effectiveness on image classification tasks with ResNet architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The low difficulty summary: This research looks at a way to find the best solution for complex problems by adding some noise and gradually improving it. The authors test this method using different types of functions and show that it works well for certain types of neural networks. They also extend this approach to another common optimization technique and demonstrate its effectiveness in image classification tasks. |
Keywords
» Artificial intelligence » Image classification » Optimization » Resnet » Stochastic gradient descent
