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Gaussian process

Known as: GP, Gaussian Processes, Gaussian stochastic process 
In probability theory and statistics, a Gaussian process is a statistical model where observations occur in a continuous domain, e.g. time or space… Expand
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Papers overview

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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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