Appendix for Learning to Predict Trustworthiness with Steep Slope Loss Y an Luo

By Hoeffding's bound, we have null The ViT (i.e., ViT Base/16) used in this work is implemented in the ASYML project The code is implemented in Python 3.8.5 with PyTorch 1.7.1 [ For the other experiments or analyses, we run one time. The implementation provides the pre-trained models on MNIST and CIFAR-10. License, while the implementation of ViT is licensed under the Apache-2.0 Ideally, we hope that all the confidences w.r.t. the positive class are on the right-hand side of the positive threshold while the ones w.r.t. the negative class are on the left-hand side of the negative The oracles that are used to generate the confidences are the ones used in Table 1. ImageNet validation set (stylized val) and the adversarial ImageNet validation set (adversarial val).