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Neural networks and Gaussian processes are complementary in their strengths and weaknesses.
“On the marginal likelihood and cross-validation”
Edwin Fong and Chris Holmes · 1905
Earlier work this paper cites.
“Functions of positive and negative type, and their connection the theory of integral equations”
James Mercer · 1909
Earlier work this paper cites.
“A Practical Bayesian Framework for Backpropagation Network”
David.. MacKay · 1992
Earlier work this paper cites.
“Bayesian Model Comparison and Backprop Nets”
David.. MacKay · 1992
Earlier work this paper cites.
“Bayesian Learning for Neural Networks”
Radford. Neal · 1995
Earlier work this paper cites.
“Gaussian processes for regression”
Christopher Williams and Carl Rasmussen · 1996
Earlier work this paper cites.
“Evaluation of Gaussian processes and other methods for non-linear regression”, 1997
Carl Rasmussen · 1997
Earlier work this paper cites.
“Computing with Infinite Networks”
Christopher.. Williams · 1998
Earlier work this paper cites.
“Introduction to Gaussian Processes”
David.. MacKay · 1998
Earlier work this paper cites.
“Information Theory, Inference and Learning Algorithms”
David.. MacKay · 2003
Earlier work this paper cites.
“A Framework for Interdomain and Multioutput Gaussian Processes”
Mark van Wilk, Vincent Dutordoir, S.. John, Artem Artemev, Vincent Adam and James Hensman · 2003
Earlier work this paper cites.
Sebastian. Ober and Laurence Aitchison · 2005
Earlier work this paper cites.
“Scattered Data Approximation”
Holger Wendland · 2005
Earlier work this paper cites.
“Gaussian Processes for Machine Learning”
Carl. Rasmussen and Christopher.. Williams · 2006
Earlier work this paper cites.
In Gaussian Processes for Machine Learning
Carl. Rasmussen and Christopher.. Williams · 2006
Earlier work this paper cites.
“Strictly Proper Scoring Rules, Prediction, and Estimation”
Tilmann Gneiting and Adrian. Raftery · 2007
Earlier work this paper cites.
“Kernel Methods for Deep Learning”
Youngmin Cho and Lawrence. Saul · 2009
Earlier work this paper cites.
“Variational Learning of Inducing Variables in Sparse Gaussian Processes”
Michalis. Titsias · 2009
Earlier work this paper cites.
“Inter-domain Gaussian Processes for Sparse Inference using Inducing Features”
Miguel Lázaro-Gredilla and Aníbal Figueiras-Vidal · 2009
Earlier work this paper cites.
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Alberto Bietti and Francis Bach · 2009
Earlier work this paper cites.
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Later among the works it cites.
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Alexander.. Matthews, Mark van der Wilk, Tom Nickson, Keisuke. Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani and James Hensman · 2017
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Later among the works it cites.
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