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Review

2018

Review

2018

Abstract Multi-output regression problems have extensively arisen in modern engineering community. This article investigates the… Expand

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Review

2017

Review

2017

Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability… Expand

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Highly Cited

2009

Highly Cited

2009

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have… Expand

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Highly Cited

2009

Highly Cited

2009

One way that artists create compelling character animations is by manipulating details of a character's motion. This process is… Expand

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Highly Cited

2007

Highly Cited

2007

In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a… Expand

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Highly Cited

2005

Highly Cited

2005

We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression… Expand

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Highly Cited

2005

Highly Cited

2005

We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the the locations of M pseudo-input… Expand

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Highly Cited

2005

Highly Cited

2005

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have… Expand

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Highly Cited

1998

Highly Cited

1998

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

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Highly Cited

1995

Highly Cited

1995

The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution… Expand

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