Zhou and colleagues introduce a series of generalized Gaussian process models for genotype-phenotype mapping. The goal was to develop models that were more powerful than standard linear models, while ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

Abstract: In order to reduce the computational complexity of Gaussian process regression models when solving the sub-problem in large-scale optimization, this study considers the historical Gaussian ...

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE ...

1 Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA. 2 Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch, Tehran, ...

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 ...

Department of Chemical Engineering, Imperial College London, London SW7 2AZ, U.K. Sargent Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, U.K.