Add to ORKGContains fulltext : 91758.pdf (publisher's version ) (Open Access)Twenty-Fifth Annual Conference on Neural Information Processing Systems, 12-17 December, 2011 Granada (Spain
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Contains fulltext : 205110.pdf (publisher's version ) (Open Access)Radboud Univers...
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation...
Inference in matrix-variate Gaussian models has major applications for multioutput prediction and jo...
Inference in matrix-variate Gaussian models has major applications for multioutput prediction and jo...
Inference in matrix-variate Gaussian models has major applications for multi-output prediction and j...
Inference in matrix-variate Gaussian models has major applications for multioutput prediction and jo...
We will discuss briefly the statistical estimation of a signal (vector, matrix, tensor...) corrupted...
We consider a matrix-valued Gaussian sequence model, that is, we observe a sequence of high-dimensio...
Khan MEE, Immer A, Abedi E, Korzepa M. Approximate Inference Turns Deep Networks into Gaussian Proce...
Inferring a graphical model or network from observational data from a large number of variables is a...
We consider the high-dimensional inference problem where the signal is a low-rank symmetric matrix w...
Abstract-We consider the Bayesian inference of a random Gaussian vector in a linear model with a Gau...
International audienceWe consider the problem of estimating the noise level sigma(2) in a Gaussian l...
International audienceWe consider the problem of estimating the noise level sigma(2) in a Gaussian l...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Contains fulltext : 205110.pdf (publisher's version ) (Open Access)Radboud Univers...
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation...
Inference in matrix-variate Gaussian models has major applications for multioutput prediction and jo...
Inference in matrix-variate Gaussian models has major applications for multioutput prediction and jo...
Inference in matrix-variate Gaussian models has major applications for multi-output prediction and j...
Inference in matrix-variate Gaussian models has major applications for multioutput prediction and jo...
We will discuss briefly the statistical estimation of a signal (vector, matrix, tensor...) corrupted...
We consider a matrix-valued Gaussian sequence model, that is, we observe a sequence of high-dimensio...
Khan MEE, Immer A, Abedi E, Korzepa M. Approximate Inference Turns Deep Networks into Gaussian Proce...
Inferring a graphical model or network from observational data from a large number of variables is a...
We consider the high-dimensional inference problem where the signal is a low-rank symmetric matrix w...
Abstract-We consider the Bayesian inference of a random Gaussian vector in a linear model with a Gau...
International audienceWe consider the problem of estimating the noise level sigma(2) in a Gaussian l...
International audienceWe consider the problem of estimating the noise level sigma(2) in a Gaussian l...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Contains fulltext : 205110.pdf (publisher's version ) (Open Access)Radboud Univers...
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation...