Latest Articles for Gaussian Processes

A guide to Bayesian analysis techniques for spatial data forecasting.

This article explores methods to enhance Gaussian processes through gradient incorporation and covariance adjustments.

A new study uncovers unexpected light delays around supermassive black holes.

A review of various methods to predict temperature changes in U.S. cities.

Scientists improve material discovery efficiency by integrating expert knowledge with Bayesian optimization.

A new approach to understanding and predicting smoking habits.

New methods enhance reliability analysis in aerospace using deep Gaussian processes.

A look at methods for analyzing large spatial datasets and their challenges.

This article reviews active learning's role in global sensitivity analysis for better data collection.

Investigating the link between dark matter, dark energy, and cosmic expansion.

A novel approach improves Bayesian optimization efficiency and reduces computation time significantly.

New framework boosts efficiency and accuracy in reduced-order modeling.

MMDPGP provides a flexible approach to clustering gene expression data from multiple replicates.

New techniques improve power flow learning for efficient electricity delivery.

This study automates the classification of scientific texts with the right ontologies.

A new framework improves quantum device measurement by addressing uncertainties.

A new approach combines physics with data-driven methods for improved structure safety.

A new method for clustering large datasets effectively and efficiently.

Examining the benefits and applications of non-stationary kernels in machine learning.

Examining the impact of Gaussian processes in complex data environments.

A new method combines data-driven techniques with physical principles for better modeling.

Innovative methods enhance voltage control in isolated power systems.

New method enhances predictions using Gaussian processes on unknown data structures.

MPHD improves Bayesian Optimization by using knowledge from different domains for better results.

A new model improves uncertainty quantification in nuclear thermal-hydraulic systems.

A novel approach enhances efficiency in Kronecker Matrix-Matrix Multiplication for machine learning tasks.

Deep Kernel Learning enhances control systems, ensuring safety and reliability amidst complexity.

This article introduces a model to simplify complex high-dimensional data using Gaussian processes.

A new method enhances simulation speed and accuracy across various scientific fields.

A novel approach enhances accuracy in estimating continuous treatment effects in various fields.

Researchers combine data sources to enhance molecular property predictions efficiently.

A new algorithm improves optimization when hyperparameters are unknown.

New methods improve detection of Earth-like planets amidst stellar noise.

A new Bayesian approach for analyzing data with many zeros in public health.

Combining diverse data sources boosts predictive modeling in materials research.

New methods enhance speed and efficiency in Gaussian processes for data prediction.

A look into dark matter and energy's roles in the universe's expansion.

New control methods improve stability for underactuated balance robots using Gaussian processes.

Scientists aim to enhance detection methods for Earth-like exoplanets outside our Solar System.

Gaussian processes offer versatile modeling tools for various data analysis applications.