Summary of Understanding Inter-concept Relationships in Concept-based Models, by Naveen Raman et al.
Understanding Inter-Concept Relationships in Concept-Based Models
by Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 ability of concept-based explainability methods in deep learning systems to capture the rich structure of inter-concept relationships. It analyzes the representations learned by these models and finds that they often lack stability and robustness, leading to a failure to accurately capture inter-concept relationships. The authors propose a novel algorithm that leverages these relationships to improve concept intervention accuracy and demonstrate its effectiveness in improving downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well deep learning systems can understand the connections between different ideas or concepts. It finds that most of these systems don’t do a great job of recognizing how these concepts relate to each other. The researchers come up with a new way to make these systems better by using the relationships between concepts to improve their performance. |
Keywords
* Artificial intelligence * Deep learning
