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Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have… Expand One way that artists create compelling character animations is by manipulating details of a character's motion. This process is… Expand In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a… Expand We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression… Expand Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have… Expand Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In… Expand We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the the locations of M pseudo-input… Expand We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c=1,...,m. For a two-class… Expand Feedforward neural networks such as multilayer perceptrons are popular tools for nonlinear regression and classification problems… Expand The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution… Expand