Summary of Information Geometry Of Evolution Of Neural Network Parameters While Training, by Abhiram Anand Thiruthummal et al.
Information Geometry of Evolution of Neural Network Parameters While Training
by Abhiram Anand Thiruthummal, Eun-jin Kim, Sergiy Shelyag
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A novel approach to enhancing the explainability and interpretability of artificial neural networks (ANNs) is presented. The study explores the application of an information geometric framework to understand phase transition-like behaviors that occur during ANN training, which are linked to overfitting in specific models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial neural networks (ANNs) can solve any mathematical problem, but they’re like black boxes – hard to understand. To fix this, many methods have been tried to make ANNs more understandable. This study uses a special way of thinking called the information geometric framework to figure out why some ANNs get really good at doing their job, and how that relates to when they start getting too good (overfitting). |
Keywords
» Artificial intelligence » Overfitting
