Summary of Sparse Explanations Of Neural Networks Using Pruned Layer-wise Relevance Propagation, by Paulo Yanez Sarmiento et al.
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
by Paulo Yanez Sarmiento, Simon Witzke, Nadja Klein, Bernhard Y. Renard
First submitted to arxiv on: 22 Apr 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 proposed approach modifies layer-wise relevance propagation (LRP) to improve explainability in deep neural networks (DNNs). By directly pruning the relevance propagation for different layers, the method achieves sparser attributions for input features and intermediate layers. This allows for pruning different neurons for different inputs, making it more suitable for local explanation methods. The approach is evaluated on image and genomic sequence data, demonstrating reduced noise and concentrated relevance on important features compared to the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about making deep neural networks (DNNs) easier to understand. Right now, people need to figure out what’s important in a DNN’s output, which can be hard, especially when dealing with complex data like genome sequences. The researchers suggest modifying an existing explanation method called layer-wise relevance propagation to make it more useful for real-world applications. They test their approach on images and genomic sequences, showing that it reduces noise and helps identify the most important features. |
Keywords
» Artificial intelligence » Pruning
