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Fine-grained Uncertainty Modeling in Neural Networks
Our method sits on top of a given Neural Network, requires a single scan of training data to estimate class distribution statistics, and is highly scalable to deep networks and wider pre-softmax layer.
Adversarial TCAV -- Robust and Effective Interpretation of Intermediate Layers in Neural Networks
For robustness, we define it as the ability of an intermediate layer to be consistent in its recall rate (the effectiveness) for different random seeds.
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