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 ...
Scientific Research Publishing

Rasmussen, C.E. and Williams, C.K.I. (2005) Gaussian Processes for Machine Learning. The MIT Press.

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
GitHub

deep-kernel-learning

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood ...
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.
Scientific Research Publishing

Olivier, B.G., Rohwer, J.M. and Hofmeyr, J.S. (2002) Modelling Cellular Processes With Python and Scipy. Molecular Biology Reports, 29, 249-254.

ABSTRACT: The origin of complex biological symmetric structures has long been a subject of interest and debate. How new sophisticated structures arise, perfectly meshed together, and added to ...
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 ...