Gaussian Processes for Machine Learning
PulseAugur coverage of Gaussian Processes for Machine Learning — every cluster mentioning Gaussian Processes for Machine Learning across labs, papers, and developer communities, ranked by signal.
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New Gaussian Process Kernel Models Rotational Anisotropy in Spatial Data
Researchers have developed a new interpretable kernel for Gaussian Processes that can model rotational anisotropy in 3D spatial fields. This kernel explicitly parameterizes principal length-scales and orientation, offer…
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Bayesian Parameter Shift Rule enhances VQE gradient estimation
Researchers have introduced a Bayesian variant of the parameter shift rule (PSR) for variational quantum eigensolvers (VQEs). This new method utilizes Gaussian processes to estimate objective function gradients, offerin…
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Researchers develop neural networks for scalable Gaussian process covariance kernels
Researchers have developed a new framework for creating scalable and flexible covariance kernels for Gaussian processes (GPs). This method directly learns the covariance structure using deep neural architectures and a r…
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New Epistemic Nearest Neighbors method speeds up Bayesian optimization
Researchers have developed Epistemic Nearest Neighbors (ENN), a novel method designed to improve the scalability of Bayesian optimization (BO) for problems with numerous observations. Unlike traditional Gaussian process…
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Gaussian Processes tutorial explores preference learning for personalized applications
This paper presents a comprehensive framework for preference learning using Gaussian Processes (GPs). It integrates principles from economics and decision theory into the machine learning process. The framework allows f…
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Researchers propose new method for predicting spatial deformation in nonstationary Gaussian processes
Researchers have developed a new method to improve nonstationary Gaussian processes (GPs) by modeling spatial deformations as a function of covariates. This approach addresses the limitations of static methods that cann…
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Diffusion models enhance Bayesian rain field reconstruction and Gaussian process inference
Researchers have developed a new method for reconstructing rainfall fields using commercial microwave links and diffusion models as spatial priors. This approach treats rain field estimation as a Bayesian inverse proble…
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Bayesian Tensor Network Kernel Machines use Laplace approximation for uncertainty estimation
Researchers have developed a new Bayesian Tensor Network Kernel Machine (LA-TNKM) that utilizes a linearized Laplace approximation for inference. This method addresses the challenge of providing uncertainty estimates in…
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Researchers unify Bayesian optimization for stationary point searches
Researchers have developed a unified Bayesian optimization framework to accelerate searches for stationary points in potential energy surfaces. This approach utilizes Gaussian process regression with derivative observat…
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New BSA-TNP model offers scalable, accurate spatiotemporal inference
Researchers have introduced a new neural process model called the Biased Scan Attention Transformer Neural Process (BSA-TNP). This architecture aims to improve scalability and accuracy for modeling complex spatiotempora…
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Gaussian Processes enable data-efficient control of nonlinear batch processes
Researchers have developed a new Gaussian Process-based Model Predictive Control (GP-MLMPC) scheme for nonlinear batch processes. This approach iteratively learns a dynamic model using data from initial batches, improvi…
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New methods enhance low-light images using Retinex and Bayesian optimization
Researchers have developed FLARE-BO, an enhanced framework for improving low-light robotic vision. This new method expands upon a previous training-free approach by optimizing eight parameters, including gamma correctio…
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New Hilbert Space Gaussian Process method speeds up sequential design
Researchers have developed a new Hilbert space Gaussian process approximation to improve sequential design in expensive simulation experiments. This novel approach allows for closed-form evaluation of the integrated mea…
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AI methods tackle complex nonlinear PDEs with sparse identification
Researchers have developed a novel framework using sparse radial basis function networks to solve nonlinear partial differential equations (PDEs). This approach incorporates sparsity-promoting regularization to manage o…
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A Visual Introduction to Machine Learning (2015)
This collection of resources offers a broad overview of machine learning, from foundational concepts and visual introductions to theoretical underpinnings and practical applications. It includes a visual guide to classi…