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- GitHub - shubiuh GPML: Gaussian Process Machine Learning
We finally include grid-based covariance approximations natively More generic sparse approximation using Power EP unified treatment of FITC approximation, variational approaches VFE and hybrids
- Grid based gaussian process speedup - Modeling - The Stan Forums
I have data sampled on a 2D regular rectangular grid, and would like to use Gaussian process methods for modelling My data can potentially be very large, so I’d like to use some kind of speedup or approximation I’ve seen a few papers articles posts mention that regular grids allow for speedup of gaussian process calculations
- Gaussian Process-based Spatio-Temporal Predictor - uni-obuda. hu
Abstract: This paper presents a grid-based algorithm using Gaussian Processes to predict outputs using spatially and temporally dependent data First, independent Gaussian Processes are formulated along space and time axes Then, these processes are coupled with a common noise in the covariance kernel
- Gaussian Process Regression with Grid Spectral Mixture Kernel . . .
Abstract: Kernel design for Gaussian processes (GPs) along with the associated hyper-parameter optimization is a challenging problem In this paper, we propose a novel grid spectral mixture (GSM) kernel design for GPs that can automatically fit multidimensional data with affordable model complexity and superior modeling capability
- Grid-based Gaussian Processes Factorization Machine for Recommender . . .
In this paper, we propose a new model called Grid-based Gaussian Processes Factorization Machine (GGPFM), which is based on Gaussian Processes (GP), to capture the nonlinear relationship between users and items The generic inference and learning algorithms for GP regression have cubic complexity with respect to the size of dataset
- Scalable Gaussian Process Structured Prediction for Grid Factor Graph . . .
Here we explore a scalable approach to learning GPstruct models based on ensemble learning, with weak learners (predictors) trained on subsets of the latent variables and bootstrap data, which can easily be distributed We show experiments with 4M latent variables on image segmentation
- GitHub - ysaatchi gp-grid: Gaussian processes over inputs which are on . . .
Gaussian processes over inputs which are on a complete or partial cartesian grid O (N) runtime and memory usage! Gilboa, Elad, Yunus Saatci, and John P Cunningham "Scaling Multidimensional Inference for Structured Gaussian Processes " to appear TPAMI (2013)
- Using 2D Gaussian process predictions within model
The model has two steps: 1) based on the current and past observed values for chosen tiles on the current grid, estimate the value of each tile (including unseen unchosen ones) using a GP, and 2) based on the values inferred from the GP and a softmax choice rule, make choices that will maximize point values
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