Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …

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 …

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source
domains to unlabeled target domains. However, transferring complex time series models …

Training state-of-the-art vision models has become prohibitively expensive for researchers
and practitioners. For the sake of accessibility and resource reuse, it is important to focus on …

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 …

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 …

Traditional human emotion recognition is based on electroencephalogram (EEG) data
collection technologies which rely on plenty of rigid electrodes and lack anti‐interference …

Unsupervised domain adaptation (UDA) aims to improve the classification performance on
an unlabeled target domain by leveraging information from a fully labeled source domain …

In this work, we present a method for unsupervised domain adaptation. Many adversarial
learning methods train domain classifier networks to distinguish the features as either a …

Health care is one of the most exciting frontiers in data mining and machine learning.
Successful adoption of electronic health records (EHRs) created an explosion in digital …