Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Abstract Domain Adaptation aims to transfer the knowledge learned from a labeled source
domain to an unlabeled target domain whose data distributions are different. However, the …

Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems
are mainly divided into three major paradigms: motor imagery (MI), event-related potential …

Source-free domain adaptation (SFDA) newly emerges to transfer the relevant knowledge of
a well-trained source model to an unlabeled target domain, which is critical in various …

In many real-world reinforcement learning applications, access to the environment is limited
to a fixed dataset, instead of direct (online) interaction with the environment. When using this …

The standard risk minimization paradigm of machine learning is brittle when operating in
environments whose test distributions are different from the training distribution due to …