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Found 2 papers, 1 papers with code
Adversarial Attacks Against Uncertainty Quantification
Machine-learning models can be fooled by adversarial examples, i. e., carefully-crafted input perturbations that force models to output wrong predictions.
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks
Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks.
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