% Q. Zhang, W. Liu, E. Tsang, and B. Virginas. Expensive Multiobjective % Optimization by MOEA/D with Gaussian Process model. IEEE Transactions on % Evolutionary ...

The discrete cosine transform (DCT) remains a cornerstone of modern image and video compression techniques, enabling the decomposition of visual data into frequency components that can be efficiently ...

Abstract: Bayesian optimization (BO) is a versatile and robust global optimization method under uncertainty. However, most of the BO algorithms were developed for problems with only continuous ...

⚡ Python library to compute an input probability query on a given Bayes net on discrete random variables using Prior sampling, Rejection Sampling, Likelihood weighting and Gibbs sampling.

Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet parameter inference for such models remains highly challenging—particularly when ...

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG), magnetoencephalography (MEG), and also from invasive ones such as ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, and statistics. These provide the foundation for developing the needed ...

In this paper, researchers from Queen Mary University of London, UK, University of Oxford, UK, Memorial University of Newfoundland, Canada, and Google DeepMind Moutain View, CA, USA proposed a ...