Summary of Assessing Simplification Levels in Neural Networks: the Impact Of Hyperparameter Configurations on Complexity and Sensitivity, by (joy) Huixin Guan
Assessing Simplification Levels in Neural Networks: The Impact of Hyperparameter Configurations on Complexity and Sensitivity
by Huixin Guan
First submitted to arxiv on: 24 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 The paper investigates the simplification properties of neural networks under varying hyperparameter configurations, focusing on Lempel Ziv complexity and sensitivity. By tweaking activation functions, hidden layers, and learning rates, the study assesses how these parameters affect network outputs’ complexity and robustness to input perturbations. The experiments utilize the MNIST dataset to explore relationships between hyperparameters, complexity, and sensitivity, enhancing our theoretical understanding of neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how changing some settings in neural networks affects how simple or complex their answers are, and how well they can handle small changes in the data they’re given. By trying out different combinations of these settings, like using different math functions to figure out what’s important or adding more layers to learn from, researchers want to understand how these choices affect the results. They used a special dataset called MNIST to test their ideas and see if it can help us better understand neural networks. |
Keywords
» Artificial intelligence » Hyperparameter
