A VAE with SHAP-based interpretability learns low-dimensional codes for Drosophila connectome subgraphs, and the codes can be steered to generate graph subgraphs with targeted edge counts and other properties.
Machine learning explainability in nasopharyngeal cancer survival using lime and shap
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Unveiling and Steering Connectome Organization with Interpretable Latent Variables
A VAE with SHAP-based interpretability learns low-dimensional codes for Drosophila connectome subgraphs, and the codes can be steered to generate graph subgraphs with targeted edge counts and other properties.