Extensions from the Gaussian Processes Perspective
Deep Gaussian Processes
... taken from the USPS handwritten digit ...ranging from 1 (equivalent to Bayesian GP-LVM) to 5 hidden layers and evaluated each model by measuring the nearest neighbour error in the latent fea- tures ...
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Detecting periodicities with Gaussian processes
... of Gaussian processes, from the combination of one-dimensional periodic kernels to obtain periodic kernels in higher dimension to the use of sparse methods when the number of observation becomes ...
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CONVEX BODIES AND GAUSSIAN PROCESSES
... (Accepted October 11, 2009) ABSTRACT For several decades, the topics of the title have had a fruitful interaction. This survey will describe some of these connections, including the GB/GC classification of convex bodies, ...
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Functional quantization of Gaussian processes
... 2.2. Finite dimensional subproblems Now wediscuss there duction of thequantization proble m to finite dimensional subspaces of H: For any finite dimensional linear subspace U of H; let P U denote the orthogonal projection ...
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Differentially Private Gaussian Processes
... We return to the example of the !Kung San women data to demonstrate the improvement in privacy efficiency. Figure 1B illustrates the results for a reasonable value of ε = 1 2 . The input domain is deliberately extended ...
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Gaussian processes for machine learning
... far from 95% but as the definition of confidence interval states and given the fact that the uncertainty region is wide we expect that in the clustering approach the results will ...improve. From a ...
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Gaussian Processes in Machine Learning
... how Gaussian processes can conveniently be used to specify very flex- ible non-linear ...drawn from GPs with particular covariance functions is an impor- tant ongoing research ...
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Gaussian Processes for Machine Learning
... There is no guarantee that the marginal likelihood does not suffer from mul- multiple local maxima tiple local optima. Practical experience with simple covariance functions seem to indicate that local maxima are ...
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Bayesian Warped Gaussian Processes
... Specially good results are obtained for german, ringnorm, and splice, though we are aware than even better results can be obtained by using an isotropic SE covariance on these data sets [19]. 5 Discussion and further ...
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Detecting periodicities with Gaussian processes
... clockwise from top left) which is close to the classification ...the Gaussian process approach show a strong periodic signal (we have for all genes S = ...the Gaussian process models cannot simply be ...
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Stein Variational Gaussian Processes
... Abstract We show how to use Stein variational gradient descent (SVGD) to carry out inference in Gaussian process (GP) models with non-Gaussian likelihoods and large data volumes. Markov chain Monte Carlo ...
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Gaussian processes for computer experiments
... framework, Gaussian Process (GP) is a popular model ...also Gaussian processes [16, ...a Gaussian process model without ...a Gaussian process model, where the inequality constraints are ...
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Relational Learning with Gaussian Processes
... “Student or Not” while yields better results on 3 out of 4 tasks of “Other or Not”. As future work, it would be interesting to utilize weighted linkages and to compare with other graph kernels. 4.3 Semi-supervised ...
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Gaussian Processes for Big Data
... Figure 9: Automatic relevance determination param- eters for the features used for predicting flight delays. runs. One of the main advantages of the approach presented here is that the computational complexity is ...
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Representations and regularity of Gaussian processes
... for Gaussian processes as well as for any second order stochastic process, it is given in terms of eigenvalues and eigenfunctions that are sometimes difficult to express explicitly even for some ...
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Gaussian Processes for Ordinal Regression
... Gaussian processes (O’Hagan, 1978; Neal, 1997) have provided a promising non-parametric Bayesian approach to metric regression (Williams and Rasmussen, 1996) and classification prob- lems (Williams and ...
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String and Membrane Gaussian Processes
... The last implementation of the idea in this section consists of distributing the training data over multiple independent but identical GP models. In regression problems, examples include the Bayesian Committee Machines ...
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Regularized Sparse Gaussian Processes
... Gaussian processes are a flexible Bayesian nonpara- metric modelling approach that has been widely ap- plied to learning tasks such as facial expression recog- nition, image reconstruction, and human pose ...
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An Application of Gaussian Processes on Ocular Artifact Removal from EEG
... of Gaussian Processes on Ocular Artifact Removal from EEG* Saman Noorzadeh 1,2 , Bertrand Rivet 1 , Pierre-Yves Gum´ery 2 Abstract— Consequences of eye movements are one of the main inferences that ...
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Optimality of Poisson Processes Intensity Learning with Gaussian Processes
... “Sigmoidal Gaussian Cox Process” (SGCP). It is by now well known both from theory and practice that nonparametric Bayesian methods need to be tuned very carefully to produce good ...
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