Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

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

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

Abstract Objective. Deep learning (DL)-based brain–computer interface (BCI) in motor
imagery (MI) has emerged as a powerful method for establishing direct communication …

In this paper, we investigate a challenging unsupervised domain adaptation setting---
unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data …

With the rapid development of computing technology, wearable devices make it easy to get
access to people's health information. Smart healthcare achieves great success by training …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …