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This tutorial aims to provide an intuitive understanding of the Gaussian processes regression.

Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction.

We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information extraction, regardless of the extent to which the node labels are homophilic or heterophilic.

This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas.

complex Gaussian) and the number of measurements is large enough ($m \ge C n \log^3 n$), with high probability, a natural least-squares formulation for GPR has the following benign geometric structure: (1) there are no spurious local minimizers, and all global minimizers are equal to the target signal $\mathbf x$, up to a global phase; and (2) the objective function has a negative curvature around each saddle point.

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.

Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data.

Mode decomposition is a prototypical pattern recognition problem that can be addressed from the (a priori distinct) perspectives of numerical approximation, statistical inference and deep learning.

Aligning signals is essential for integrating fragmented knowledge in each signal or resolving signal classification problems.

In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model.