This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Machinery prognostics is one of the major tasks in condition based maintenance (CBM),
which aims to predict the remaining useful life (RUL) of machinery based on condition …

Neuroimaging has greatly enhanced the cognitive neuroscience understanding of the
human brain and its variation across individuals (neurodiversity) in both health and disease …

This paper introduces a general Bayesian framework for obtaining sparse solutions to
regression and classification tasks utilising models linear in the parameters. Although this …

The data of interest are assumed to be represented as N-dimensional real vectors, and
these vectors are compressible in some linear basis B, implying that the signal can be …

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received
much attention in the machine learning literature as a means of achieving parsimonious …

The influence of this Thomas Bayes' work was immense. It was from here that" Bayesian"
ideas first spread through the mathematical world, as Bayes's own article was ignored until …

The'sparse Bayesian'modelling approach, as exemplified by the'relevance vector machine',
enables sparse classification and regression functions to be obtained by linearlyweighting a …

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility
of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a …