Summary of Energy-based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations, by Xinyue Xu et al.
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
by Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 proposes a new approach to understanding black-box deep learning models by developing Energy-based Concept Bottleneck Models (ECBMs). Existing methods, such as concept bottleneck models (CBMs), have limitations in capturing high-order interactions between concepts and quantifying conditional dependencies. ECBMs address these limitations by defining the joint energy of candidate tuples using neural networks. This approach enables prediction, concept correction, and conditional dependency quantification, providing richer concept interpretations. Empirical results show that ECBMs outperform state-of-the-art methods on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper wants to help us better understand how deep learning models work by creating a new way of looking at them. Right now, we can’t always figure out why they make the decisions they do. The researchers are trying to fix this by making a special kind of model that looks at lots of things all at once and tells us what’s important. This will help us understand how the models are working and make them better. |
Keywords
* Artificial intelligence * Deep learning
