Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

Abstract: Current saliency map interpretations for neural networks generally rely on two key assumptions. First, they use first-order approximations of the loss function, neglecting higher-order terms such a...
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