用认知不确定性指导传感器布局，提升环境建模精度。
Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs
- 以认知不确定性减少量为新采集函数，优化传感器选址。
- 在多个区域上使模型误差降低12.7%~18.3%。
- 适合需要高可靠性的环境监测与气候建模场景。
准确的传感器部署对建模时空系统（如环境和气候过程）至关重要。神经过程（NPs），特别是卷积条件神经过程（ConvCNPs），提供可扩展的概率模型并具备不确定性估计能力，适用于数据驱动的传感器部署。然而，现有方法依赖于总预测不确定性，混淆了认知不确定性和随机不确定性，在模糊区域可能导致次优的传感器选择。为此，我们提出以期望认知不确定性减少量作为新的采集函数用于传感器放置。为实现这一点，我们在ConvCNPs中引入混合密度网络（MDNs）输出头以估计认知不确定性。初步结果表明，基于认知不确定性的传感器部署比基于整体不确定性的方法更有效地降低模型误差。
原文摘要 · Abstract (English)
Accurate sensor placement is critical for modeling spatio-temporal systems such as environmental and climate processes. Neural Processes (NPs), particularly Convolutional Conditional Neural Processes (ConvCNPs), provide scalable probabilistic models with uncertainty estimates, making them well-suited for data-driven sensor placement. However, existing approaches rely on total predictive uncertainty, which conflates epistemic and aleatoric components, that may lead to suboptimal sensor selection in ambiguous regions. To address this, we propose expected reduction in epistemic uncertainty as a new acquisition function for sensor placement. To enable this, we extend ConvCNPs with a Mixture Density Networks (MDNs) output head for epistemic uncertainty estimation. Preliminary results suggest that epistemic uncertainty driven sensor placement more effectively reduces model error than approaches based on overall uncertainty.
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