Summary of Training Neural Networks For Modularity Aids Interpretability, by Satvik Golechha et al.
Training Neural Networks for Modularity aids Interpretability
by Satvik Golechha, Dylan Cope, Nandi Schoots
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 A novel approach to improving network interpretability is proposed, which involves splitting a model into disjoint clusters that can be studied independently. The authors identify that pre-trained models are highly unclusterable and develop an “enmeshment loss” function to train models that form non-interacting clusters. By using automated interpretability measures, the study shows that this method finds clusters that learn different, smaller circuits for CIFAR-10 labels. This approach provides a promising direction for making neural networks easier to interpret. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to make artificial intelligence (AI) models more understandable. Usually, AI models are very hard to understand because they are too connected and complex. To fix this, the researchers created a special kind of loss function that helps train AI models to be more modular and easy to understand. They tested their approach on a popular image recognition task called CIFAR-10 and found that it worked well. This breakthrough could help people better understand how AI models work. |
Keywords
* Artificial intelligence * Loss function
