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Found 4 papers, 1 papers with code
Gaussian Processes for Monitoring Air-Quality in Kampala
Monitoring air pollution is of vital importance to the overall health of the population.
Modelling calibration uncertainty in networks of environmental sensors
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift.
Adjoint-aided inference of Gaussian process driven differential equations
In this paper we show how the adjoint of a linear system can be used to efficiently infer forcing functions modelled as GPs, using a truncated basis expansion of the GP kernel.
Machine Learning for a Low-cost Air Pollution Network
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used.
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