Gaussian Processes
PulseAugur coverage of Gaussian Processes — every cluster mentioning Gaussian Processes across labs, papers, and developer communities, ranked by signal.
- 2026-05-20 research_milestone A new paper proposes a method to condition Gaussian Processes on natural language and other complex data. 来源
4 天有情绪数据
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New VIF method boosts Gaussian process scalability and accuracy
Researchers have developed a new approximation method called Vecchia-Inducing-Points Full-Scale (VIF) to improve the scalability of Gaussian processes. This approach combines global inducing points with local Vecchia ap…
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Physics-informed ML reconstructs aerodynamic loads from bridge data
Researchers have developed a probabilistic physics-informed machine learning method to reconstruct aerodynamic loads from noisy structural response data. This approach, demonstrated on the Great Belt East Bridge, avoids…
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Gaussian Processes now condition on natural language via diffusion models
Researchers have developed a novel method to condition Gaussian Processes (GPs) on a wide range of information, including natural language. This approach establishes an equivalence between GPs and linear diffusion model…
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Vecchia approximations lead in Gaussian process accuracy-runtime comparison
Researchers have compared various scalable Gaussian process approximations for handling large spatial datasets. Their analysis focused on the trade-off between model accuracy and computational runtime across simulated a…
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New method corrects Bayesian inference errors in latent Gaussian models
Researchers have developed a new method to correct errors in Bayesian inference for latent Gaussian models. The proposed importance sampling scheme improves the accuracy of approximate posteriors derived from integrated…
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New tcGP method improves Gaussian Process calibration for Bayesian Optimization
Researchers have developed a new method called tcGP to improve the calibration of Gaussian Process (GP) predictive distributions, specifically focusing on lower-tail calibration. This is crucial for Bayesian Optimizatio…
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Matérn Gaussian Processes extended for graph-based machine learning
Researchers have developed a new class of Gaussian processes specifically designed for undirected graphs, extending a versatile framework for learning unknown functions. These Matérn Gaussian processes on graphs inherit…
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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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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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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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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…