Gaussian process
PulseAugur coverage of Gaussian process — every cluster mentioning Gaussian process across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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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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New active learning method tackles complex Boltzmann distributions
Researchers have developed a new Gaussian Process-based acquisition function called AB-SID-iVAR for active learning problems. This method addresses the challenge of learning an unknown function under a self-induced Bolt…
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New Gaussian process framework uses neural feature maps for scalable inference
Researchers have developed a new Gaussian process framework that uses neural feature maps to create more expressive kernels. This method allows for efficient and accurate Gaussian process inference, applicable to both r…
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New algorithm optimizes Gaussian process posterior mean functions efficiently
Researchers have developed PALM-Mean, a new algorithm designed for the global optimization of Gaussian Process posterior mean functions. This method employs a hybrid approach, combining piecewise-analytic lower bounds w…
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Review details multi-fidelity neural networks for composite mechanics modeling
This paper reviews multi-fidelity surrogate modeling techniques for predicting the complex properties of composite materials. It covers methods ranging from Gaussian-process-based approaches like co-Kriging to multi-fid…
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Safe Active Learning framework autonomously qualifies Ga2O3 sensors
Researchers have developed a Safe Active Learning (SAL) framework to autonomously characterize the reliability of Ga$_2$O$_3$-based devices under stress. This framework uses a Gaussian-process surrogate model to track d…
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Spline networks achieve distance-aware uncertainty bounds for safer AI navigation
Researchers have introduced a novel method for quantifying uncertainty in spline neural networks, termed distance-aware error bounds. This bottom-up approach analyzes individual neuron errors to determine network-wide a…
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Deep Kernel Learning stratifies glaucoma patient risk trajectories using EHR data
Researchers have developed a new deep kernel learning architecture to help stratify glaucoma patient risk using electronic health records. The model employs a transformer-based feature extractor with clinical-BERT embed…
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An adaptive wavelet-based PINN for problems with localized high-magnitude source
Researchers have developed an adaptive wavelet-based physics-informed neural network (AW-PINN) to address limitations in solving differential equations, particularly those with localized high-magnitude source terms. Thi…
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New Gaussian Process Model Enhances Interpretable Clinical Time Series Forecasting
Researchers have developed StructGP, a novel Gaussian process model designed for interpretable forecasting in clinical time series. This model couples process convolutions with differentiable structure learning to uncov…
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New Bayesian optimization method minimizes worst-case functional errors
Researchers have introduced a new framework called min-max Functional Bayesian Optimization (MM-FBO) to address challenges in optimizing functions with functional responses, which are common in scientific and engineerin…
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New HPPCA model improves analysis of longitudinal data with missing values
Researchers have developed Hierarchical Probabilistic Principal Component Analysis (HPPCA), a novel statistical model designed to handle complex longitudinal data with missing values. This two-level probabilistic factor…
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New Kernel Score Enhances Multivariate Conformal Prediction Regions
Researchers have developed a new Multivariate Kernel Score (MKS) for conformal prediction, designed to better handle multivariate data. This score compresses residual vectors into scalars while preserving geometric info…