Results 31 to 40 of about 300,432 (267)

Global Optimization Employing Gaussian Process-Based Bayesian Surrogates

Entropy, 2018
The simulation of complex physics models may lead to enormous computer running times. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner.
Roland Preuss, Udo von Toussaint
doaj   +1 more source

Gaussian Process Boosting

J. Mach. Learn. Res., 2020
We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean function in Gaussian process and grouped random effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms.
openaire   +4 more sources

Convolutional Gaussian Processes

CoRR, 2017
To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017)
van der Wilk, Mark, Rasmussen, Carl Edward, Hensman, James   +2 more
openaire   +3 more sources

Deep Gaussian Processes

CoRR, 2012
In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We
Damianou, A.C., Lawrence, N.D.
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Gaussian Process for Trajectories

, 2023
The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories.
Kien Nguyen 0003, John Krumm, Cyrus Shahabi   +2 more
openaire   +2 more sources

Skew Gaussian processes for classification [PDF]

Machine Learning, 2020
AbstractGaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that
Alessio Benavoli, Dario Azzimonti, Dario Piga   +2 more
openaire   +2 more sources

Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

Applied Sciences, 2019
Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of ...
Zhengmin Kong, Zhou Xia, Yande Cui, He Lv   +3 more
doaj   +1 more source

Gaussian process deconvolution

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023
Let us consider the deconvolution problem, i.e. to recover a latent source x ( ⋅ )
Felipe Tobar, Arnaud Robert, Jorge F. Silva   +2 more
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Additive Gaussian Processes

CoRR, 2011
Appearing in Neural Information Processing Systems ...
Duvenaud, D., Nickisch, H., Rasmussen, C.   +2 more
openaire   +5 more sources

Chained Gaussian Processes

CoRR, 2016
Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formalism is restrictive, link functions
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence   +3 more
openaire   +4 more sources