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Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration
[article]
2021
arXiv
pre-print
To facilitate a wide-spread acceptance of AI systems guiding decision making in real-world applications, trustworthiness of deployed models is key. That is, it is crucial for predictive models to be uncertainty-aware and yield well-calibrated (and thus trustworthy) predictions for both in-domain samples as well as under domain shift. Recent efforts to account for predictive uncertainty include post-processing steps for trained neural networks, Bayesian neural networks as well as alternative
arXiv:2012.10923v2
fatcat:gsjqr5exqbffvigwy7jzowlwcm