论文标题
深度高斯的空气质量推理
Deep Gaussian Processes for Air Quality Inference
论文作者
论文摘要
空气污染每年约有700万人丧生，约有24亿人受到危险空气污染。准确，细粒度的空气质量（AQ）监测对于控制和减少污染至关重要。但是，AQ站的部署很少，因此对未监视位置的空气质量推断至关重要。常规的插值方法无法学习复杂的AQ现象。这项工作表明，深高斯过程模型（DGP）是AQ推理任务的有前途的模型。我们实现了双随机变量推断，DGP算法，并表明它与最新模型相当。
Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.
