Summary of Deepcshap: Utilizing Shapley Values to Explain Deep Complex-valued Neural Networks, by Florian Eilers and Xiaoyi Jiang
DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
by Florian Eilers, Xiaoyi Jiang
First submitted to arxiv on: 13 Mar 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 This paper proposes novel methods to explain the output of Complex-Valued Neural Networks (CVNNs), which have emerged as a new class of neural networks dealing with complex-valued input data. CVNNs are increasingly used in safety-critical applications like healthcare and autonomous driving, where transparency is crucial. The authors focus on adapting the widely-used DeepSHAP algorithm to the complex domain and also present versions of four gradient-based explanation methods suitable for CVNNs. The algorithms’ quality is evaluated and all presented as an open-source library adaptable to most recent CVNN architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how neural networks that deal with imaginary numbers can explain their decisions. These special networks are used in important areas like healthcare and self-driving cars, where it’s crucial we know why they make certain choices. The researchers take a popular way of explaining neural network results and adapt it to work with these complex networks. They also come up with new methods for explaining the output of these networks. They test how well each method works and share all their results so others can use them. |
Keywords
* Artificial intelligence * Neural network
