Gaussian processes (GPs) have experienced tremendous success in biogeophysical
parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to …

Gaussian processes (GPs) are versatile tools that have been successfully employed to solve
nonlinear estimation problems in machine learning but are rarely used in signal processing …

Abstract Deep Gaussian processes (DGPs) are multi-layer generalizations of GPs, but
inference in these models has proved challenging. Existing approaches to inference in DGP …

Abstract Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of
Gaussian processes (GPs) and are formally equivalent to neural networks with multiple …

The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-
linear dimensionality reduction that has been widely applied. However, the current approach …

We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel scalable …

Many applications in speech, robotics, finance, and biology deal with sequential data, where
ordering matters and recurrent structures are common. However, this structure cannot be …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Uncertainty propagation across components of complex probabilistic models is vital for
improving regularisation. Unfortunately, for many interesting models based on non-linear …

An accurate estimation of biophysical variables is the key to monitor our Planet. Leaf
chlorophyll content helps in interpreting the chlorophyll fluorescence signal from space …