IEEE

Multiple Model Recursive Gaussian Process for Robust Target Tracking

Abstract: Accurate tracking of targets is vital for safe and reliable operations, particularly in complex and dynamic environments such as urban areas. Traditional tracking methods, including Kalman ...
The White House

Fact Sheet: President Donald J. Trump Begins Process to Align U.S. Core Childhood Vaccine Recommendations with Best Practices from Peer, Developed Countries

ALIGNING THE CHILDHOOD VACCINE SCHEDULE WITH INTERNATIONAL BEST PRACTICES: Today, President Donald J. Trump signed a Presidential Memorandum to begin the process to align U.S. core childhood vaccine ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...
SciELO

Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment

Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from ...
IEEE

Gaussian Process Latent Variable Modeling for Few-Shot Time Series Forecasting

Abstract: Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT systems.
C&EN

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Satellite data provides essential insights into the spatiotemporal distribution of CO ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
GitHub

Efficient Graph Condensation via Gaussian Process (GCGP)

Graph condensation reduces graph sizes while maintaining performance, addressing the scalability challenges of GNNs caused by computational inefficiencies on large datasets. Existing methods often ...