Contact person: Geir Storvik Keywords: Bayesian methodology, Machine learning, neural nets Research group: Statistics and Data Science Department of Mathematics Bayesian approaches to machine learning ...

This paper proposes a novel gas pipeline failure risk assessment model based on Bayesian network optimized by machine learning. Firstly, the pipeline fault tree model is constructed according to the ...

A research group has developed SPACIER, an advanced polymer material design tool that integrates machine learning with ...

The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, ...

The KAIST team employed the multi-objective Bayesian optimization machine learning algorithm. This algorithm learned from simulated geometries to predict the best possible geometries for enhancing ...

This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion. After some recent success of Bayesian methods in machine-learning ...

The KAIST team employed the multi-objective Bayesian optimization machine learning algorithm. This algorithm learned from simulated geometries to predict the best possible geometries for enhancing ...

An interview with Karl Friston, a computational psychiatrist and an architect of an AI developed to emulate natural ...

optimisation for machine learning, kernel methods, information theory, federated learning, and scalable models and tools for linking massive and distributed multimodal data. Our work on computational ...