X Y the available training set, diverse, ranging from single point estimates N being the number of training instances. In Bayesian (often averaged over prediction samples) to Neural Networks (BNNs) (Buntine and Weigend, 1991; predictive distributions, to set-valued or Neal, 2012; Jospin et al., 2022; Kingma and Welling, credal-set representations. We propose a novel 2013), this uncertainty is explicitly represented through unified evaluation framework for uncertaintyaware posterior predictive distributions over the parameter classifiers, applicable to a wide range space. In Deep Ensembles (DEs) (Lakshminarayanan of model classes, which allows users to tailor et al., 2017), a predictive distribution is formed by the trade-off between accuracy and precision aggregating the individual predictions generated by of predictions via a suitably designed performance multiple independently trained models.

Uncertainty estimation is increasingly attractive for improving the reliability of neural networks. In this work, we present novel credal-set interval neural networks (CreINNs) designed for classification tasks. CreINNs preserve the traditional interval neural network structure, capturing weight uncertainty through deterministic intervals, while forecasting credal sets using the mathematical framework of probability intervals. Experimental validations on an out-of-distribution detection benchmark (CIFAR10 vs SVHN) showcase that CreINNs outperform epistemic uncertainty estimation when compared to variational Bayesian neural networks (BNNs) and deep ensembles (DEs). Furthermore, CreINNs exhibit a notable reduction in computational complexity compared to variational BNNs and demonstrate smaller model sizes than DEs.