[PDF] Top 20 Sparse Spectrum Gaussian Process Regression
Has 10000 "Sparse Spectrum Gaussian Process Regression" found on our website. Below are the top 20 most common "Sparse Spectrum Gaussian Process Regression".
Sparse Spectrum Gaussian Process Regression
... the Sparse Spectrum Gaussian Process (SSGP) algorithm, a novel perspective on sparse GP approximations where rather than the usual sparsity approximation in the spatial do- main, it is ... See full document
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Gaussian process regression model for normalization of LC-MS data using scan-level information
... To summarize, first we find the base peaks for the corre- sponding mass of each ion to form the EIC. This can be done by using XCMS2, to find regions of interest [18] (ROI) for the ions selected in pre-processing. ... See full document
12
Sparse Gaussian Process Emulators for surrogate design modelling
... a Gaussian process (GP) regression model to simulator evaluations at a small number of predefined inputs in order to learn the simulator’s input to output mapping ... See full document
15
Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
... GP regression has received much attention in machine learn- ing and spatial ...GP regression is the inversion of a big sample covariance matrix of size N × N , many approaches proposed to approximate the ... See full document
29
Patchwork Kriging for Large-scale Gaussian Process Regression
... GP regression estimate for large N ...GP regression is the inversion of a N × N sample covariance matrix, which is also often poorly numerically ...as sparse approximations, low-rank approximations, ... See full document
43
pyGPs -- A Python Library for Gaussian Process Regression and Classification
... pyGPs is released under the FreeBSD license and it can be downloaded from http:// mloss.org/software/view/509/ or https://github.com/marionmari/pyGPs . pyGPs re- quires Python 2.6 or 2.7 (www.python.org) and the numpy ... See full document
6
Sparse Additive Gaussian Process with Soft Interactions
... a Gaussian process prior. To induce sparsity within each Gaussian process, we introduce an additional level of soft shrinkage ...additive Gaussian process proposed by Qamar and ... See full document
23
Gaussian Kullback-Leibler Approximate Inference
... investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various ...of Gaussian covariance that make G-KL methods fast and ... See full document
48
Ensemble Kalman filtering for online Gaussian process regression and learning
... training sparse Gaussian process models is ...the Gaussian process as the state and parameters of the ensemble Kalman filter, ...with Gaussian processes preserving the accuracy ... See full document
9
Compressed Gaussian Process for Manifold Regression
... for regression, typically em- ploying a two stage ...standard regression and classification procedures as if they were observed ...up sparse optimization methods, such as Lasso, to large p and n ... See full document
26
A Unifying View of Sparse Approximate Gaussian Process Regression
... Regression models based on Gaussian processes (GPs) are simple to implement, flexible, fully probabilistic models, and thus a powerful tool in many areas of application. Their main limitation is that memory ... See full document
21
Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors
... new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian ...the sparse signal of interest. A hierarchical Gaussian ... See full document
15
Statistical Methods for Computer Experiments with Applications in Neuroscience
... laGP is an R package (Gramacy, 2016) that provides approximate GP regression for extensive computer experiments and spatial databases. The approximation method finds small local designs (independently) to predict ... See full document
33
Gaussian Processes for Ordinal Regression
... experiments for the three algorithms. As not all ratings are observed in the input vectors, we con- sider two ad hoc strategies to deal with missing values: mean imputation and weighted low-rank approximation. In the ... See full document
23
A Gaussian process regression for natural gas consumption prediction based on time series data
... autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (tempera- ture) and calendar (day of the week, ... See full document
8
Communication-efficient Sparse Regression
... We devise a communication-efficient approach to distributed sparse regression in the high- dimensional setting. The key idea is to average “debiased” or “desparsified” lasso esti- mators. We show the ... See full document
30
Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets
... The second school approximates the likelihood of training and testing points by assuming condi- tional independence of training and testing points, given M artificial points, known as “inducing in- puts.” Under this ... See full document
32
Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach
... Amongst the three standard compliant feedback schemes, only the subband level technique allows the base station to investigate the channel quality of the complete bandwidth with equal amount of detail between sub- bands. ... See full document
14
Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression
... training process of GPR models because the central role in the Gaussian process regression model belongs to the covariance function K(x, ... See full document
21
Embarrassingly Parallel Inference for Gaussian Processes
... Lastly, we are interested to see how different GP methods compare with regards to the total wall clock time for fitting the model. Using the same data set, with J = 128 and B = 1000 for SVI and IS-MOE, we evaluate the ... See full document
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