  This paper introduces the "Uncertainty-aware Mixture of Experts" (uMoE), a
novel solution aimed at addressing aleatoric uncertainty within Neural Network
(NN) based predictive models. While existing methodologies primarily
concentrate on managing uncertainty during inference, uMoE uniquely embeds
uncertainty into the training phase. Employing a "Divide and Conquer" strategy,
uMoE strategically partitions the uncertain input space into more manageable
subspaces. It comprises Expert components, individually trained on their
respective subspace uncertainties. Overarching the Experts, a Gating Unit,
leveraging additional information regarding the distribution of uncertain
in-puts across these subspaces, dynamically adjusts the weighting to minimize
deviations from ground truth. Our findings demonstrate the superior performance
of uMoE over baseline methods in effectively managing data uncertainty.
Furthermore, through a comprehensive robustness analysis, we showcase its
adaptability to varying uncertainty levels and propose optimal threshold
parameters. This innovative approach boasts broad applicability across diverse
da-ta-driven domains, including but not limited to biomedical signal
processing, autonomous driving, and production quality control.
