This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …

With the large-scale commercialization and growing market share of electric vehicles (EVs),
many studies have been dedicated to battery systems design and development. Their focus …

In a wide range of multimodal tasks, contrastive learning has become a particularly
appealing approach since it can successfully learn representations from abundant …

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, which poison the training
set to alter the model prediction over samples with a specific trigger. While existing efforts …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

A majority of previous methods for multimodal representation learning ignore the rich
correlation information inherently stored in each sample, leading to a lack of robustness …