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 ...
IEEE

A Gaussian Process-Regularized Graphical Learning Method for Distribution System State Estimation Using Extremely Scarce State Variable Labels

Abstract: Learning-based distribution system state estimation (DSSE) methods typically depend on sufficient fully labeled data to construct mapping functions. However, collecting historical labels ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
GitHub

Python: Process Framework w/ agents can get in infinite loop if sharing the same Kernel in Python

Sharing the same Kernel when using the Process Framework can result in an infinite loop as the various steps may try and invoke each other over and over again. Either add checks to ensure agents & ...
Scientific Research Publishing

Multi-Task Gaussian Process for Imputing Missing Daily Rainfall Data Using Nearby Stations: Case of Burkina Faso ()

1 Ecole Polytechnique de Ouagadougou, Ouagadougou, Burkina Faso. 2 Agence National Meteorologie, Ouagadougou, Burkina Faso. Precipitation is a critical meteorological factor that significantly impacts ...
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 ...
IEEE

Sparsity-Aware Distributed Learning for Gaussian Processes With Linear Multiple Kernel

Abstract: Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyperparameter optimization. This article ...