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.

翻译：本文提出了一种名为“不确定性感知专家混合”（uMoE）的新型解决方案，旨在解决基于神经网络（NN）的预测模型中的偶然不确定性。现有方法主要关注推理过程中的不确定性管理，而uMoE则独特地将不确定性嵌入到训练阶段。通过采用“分治”策略，uMoE将不确定的输入空间策略性地划分为更易于管理的子空间。它由多个专家组件构成，每个组件针对其对应子空间的不确定性进行独立训练。在这些专家之上，一个门控单元利用关于不确定输入在各子空间中分布的额外信息，动态调整权重以最小化与真实值的偏差。我们的研究结果表明，uMoE在有效管理数据不确定性方面优于基线方法。此外，通过全面的鲁棒性分析，我们展示了其适应不同不确定性水平的能力，并提出了最优阈值参数。这种创新方法在多种数据驱动领域具有广泛适用性，包括但不限于生物医学信号处理、自动驾驶和生产质量控制。