Guaranteed Optimal Compositional Explanations for Neurons
📰 ArXiv cs.AI
Learn to generate guaranteed optimal compositional explanations for neurons in AI models using logical rules and spatial alignment
Action Steps
- Compute the spatial alignment between neurons' receptive field activations and concepts using logical rules
- Apply beam search to efficiently explore the state space and identify optimal combinations
- Implement a search algorithm to find the optimal compositional explanation
- Evaluate the generated explanation using metrics such as accuracy and fidelity
- Refine the explanation by iterating on the search process and adjusting the logical rules
Who Needs to Know This
AI researchers and engineers working on explainability and interpretability of neural networks can benefit from this technique to provide more accurate and reliable explanations
Key Insight
💡 Compositional explanations can be generated using logical rules and spatial alignment to provide accurate and reliable insights into neural network behavior
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Key Takeaways
Learn to generate guaranteed optimal compositional explanations for neurons in AI models using logical rules and spatial alignment
Full Article
Title: Guaranteed Optimal Compositional Explanations for Neurons
Abstract:
arXiv:2511.20934v2 Announce Type: replace Abstract: Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and concepts through logical rules, typically computed via a search over all possible concept combinations. Since computing the spatial alignment over the entire state space is computationally infeasible, the literature commonly adopts assumptions related to the structure of the combinations and beam search
Abstract:
arXiv:2511.20934v2 Announce Type: replace Abstract: Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and concepts through logical rules, typically computed via a search over all possible concept combinations. Since computing the spatial alignment over the entire state space is computationally infeasible, the literature commonly adopts assumptions related to the structure of the combinations and beam search
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