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Found 5 papers, 2 papers with code
Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets
Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs.
Modelling calibration uncertainty in networks of environmental sensors
Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift.
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also accurately quantifying uncertainty is a challenging task, which often requires problem-specific techniques.
Switched Latent Force Models for Movement Segmentation
Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function.
Sparse Convolved Gaussian Processes for Multi-output Regression
We present a sparse approximation approach for dependent output Gaussian processes (GP).
