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Found 2 papers, 1 papers with code
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis
We propose a novel approach for producing global explanations for GNNs using neuron-level concepts to enable practitioners to have a high-level view of the model.
Efficient privacy-preserving inference for convolutional neural networks
Others have proposed the use of model pruning and efficient data representations to reduce the number of HE operations required.
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