Gaussian process
PulseAugur coverage of Gaussian process — every cluster mentioning Gaussian process across labs, papers, and developer communities, ranked by signal.
7 天有情绪数据
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New methods automate Bayesian optimization for high-dimensional problems
Researchers have developed new methods to improve Bayesian optimization, a technique used for optimizing complex functions. One approach, Dynamic Shared Embedding Bayesian Optimization (DSEBO), automatically adjusts the…
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New BALLAST method enhances oceanographic vector field inference
Researchers have developed a new active learning methodology called BALLAST to improve the inference of time-dependent vector fields, particularly for oceanography. This method uses a physics-informed Gaussian process s…
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Research paper details three costs of amortizing Gaussian Process inference
A new research paper details three primary costs associated with amortizing Gaussian Process inference using Neural Processes. The study identifies label contamination, an information bottleneck, and amortization error …
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New framework models temporal single-cell RNA data with Gaussian process and optimal transport
Researchers have developed a new generative framework to model temporal processes in single-cell RNA sequencing data. This approach utilizes a latent heteroscedastic Gaussian process, approximated via Hilbert space meth…
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New research advances optimization and reinforcement learning theory
Researchers have developed new theoretical frameworks for optimizing decision-making processes in machine learning. One paper introduces regret-based stopping criteria for Bayesian optimization, ensuring solutions are w…
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New DeRegiME model improves probabilistic forecasting under distribution shift
Researchers have developed DeRegiME, a novel probabilistic forecasting method designed to handle distribution shifts in time series data. This approach uses a deep mixture of experts model with a sparse variational Gaus…
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New theory generalizes regularization for wide neural networks
A new paper introduces a novel framework for understanding and generalizing regularization in wide neural networks. The research identifies that standard ridge regularization can distort the inductive bias of feature-le…
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New Gaussian Process model generates approximately periodic time series
Researchers have developed a new generative model for time series data that exhibits approximately periodic behavior. This model utilizes a Gaussian Process (GP) with a novel kernel to effectively capture both the commo…
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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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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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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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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…