Risk-aware evidential classifiers that learn a per-sample prior from a misclassification cost matrix lower expected cost on MNIST and CIFAR10 versus cost-sensitive baselines.
Deep, spatially coherent inverse sensor models with uncertainty incorporation using the evidential framework
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Risk-aware Classification via Uncertainty Quantification
Risk-aware evidential classifiers that learn a per-sample prior from a misclassification cost matrix lower expected cost on MNIST and CIFAR10 versus cost-sensitive baselines.