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Found 3 papers, 1 papers with code
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Our approach enforces sparsity directly by pruning the relevance propagation for the different layers.
Identifying Drivers of Predictive Aleatoric Uncertainty
Overall, we explain uncertainty estimates with little modifications to the model architecture and demonstrate that our approach competes effectively with more intricate methods.
SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data
Training sophisticated machine learning (ML) models requires large datasets that are difficult or expensive to collect for many applications.
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