Summary of Cross-entropy Optimization For Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks, by Kevin Li et al.
Cross-Entropy Optimization for Hyperparameter Optimization in Stochastic Gradient-based Approaches to Train Deep Neural Networks
by Kevin Li, Fulu Li
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 cross-entropy optimization method for hyperparameter optimization in stochastic gradient-based approaches to train deep neural networks. The method optimizes hyperparameters using cross-entropy loss, which is typically used for classification tasks. The algorithm’s performance metrics include convergence speed and generalization performance, which are critical for model evaluation. The authors apply the CEHPO method within the framework of expectation maximization (EM) and demonstrate its applicability to other areas of optimization problems in deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make neural networks better by adjusting special settings called hyperparameters. These settings can make or break how well a model works. The researchers developed a new method to optimize these settings using a technique called cross-entropy. They tested their method and showed it can be used in different areas of machine learning and beyond. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Deep learning » Generalization » Hyperparameter » Machine learning » Optimization
