Latest Articles for Gaussian Processes

A new framework efficiently solves PDEs while addressing uncertainties.

A novel approach combining reinforcement learning and Bayesian optimization for better decision-making.

A look into how Bayesian optimization addresses high-dimensional challenges.

A new method enhances how robots map their environments using advanced technologies.

A new method improves predictions in complex systems using deep learning and Gaussian processes.

This article explores how drones locate radio signals autonomously.

Learn how DESI overcomes atmospheric turbulence for precise astronomical measurements.

A new method improves GP models to handle complex 3D printed designs.

A new method for estimating conditional expectations efficiently in uncertain environments.

Researchers improve detection methods for the faint 21 cm signal from deep space.

Scientists use machine learning to improve predictions in cancer drug combinations.

A new method for tuning hyperparameters using Bayesian ideas.

This research enhances Gaussian Processes using adaptive Fourier features for better predictions.

Analyzing the relationship between Gaussian processes and reproducing kernel Hilbert spaces.

Scientists analyze brain images to study cognitive diseases like Alzheimer's.

A new approach to distinguish blended stars in astronomical images through machine learning.

A new approach to topology optimization using machine learning enhances design efficiency.

Learn how Gaussian Processes improve predictions and manage uncertainty in data analysis.

Introducing a novel approach to tackle complex optimization challenges in various fields.

This article highlights the use of Gaussian Processes in clinical research analysis.

A new Bayesian approach enhances Gaussian processes for better prediction and uncertainty handling.

New models enhance video generation by capturing crucial spatial and temporal relationships.

GP-KAN merges Gaussian Processes and Kolmogorov-Arnold Networks for efficient predictions.

A new method enhances reduced-order models using Bayesian learning and Gaussian processes.

An overview of Gaussian processes, kernels, and their significance in quantum theory and data analysis.

Exploring methods to predict future events from constantly flowing data.

This study improves active learning using derivatives for better predictions.

GRBs offer key insights into the universe's expansion and distance measurement.

A new method enhances classification by adjusting models to evolving data patterns.

Learn how Gaussian processes address prediction uncertainty in machine learning.

New method enhances long-term wind power forecasts using turbine locations and climate data.

New method enhances Gaussian process models for faster predictions with real-time data.

This study presents methods for Bayesian optimization across different search spaces.

Research improves predictive modeling for safe nuclear waste storage in rock salt formations.

A method to enhance simulations using Bayesian ideas and active learning.

A new method to improve sampling in complex Bayesian inverse problems.

A new method speeds up material phase study using active learning.

A new model improves ECG analysis by integrating accurate classification and uncertainty estimation.

This article discusses how combining methods improves operator learning effectiveness.

Research combines machine learning and physics to improve star cluster simulation accuracy.