arXiv:2211.10174
공식 코드 · Papers with Code
Deep Gaussian Processes for Air Quality Inference
원문 초록
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.
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Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
Koopman-Equivariant Gaussian Processes
토론
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