The global population is aging due to many factors, including longer life expectancy through
better healthcare, changing diet, physical activity, etc. We are also witnessing various …

Time-series data contains temporal order information that can guide representation learning
for predictive end tasks (eg, classification, regression). Recently, there are some attempts to …

Multi-view learning (MVL) is a promising field that seeks to make the most of information
shared across different views, ultimately improving generalization performance. Numerous …

Anomaly detection in videos is a significant yet challenging problem. Previous approaches
based on deep neural networks employ either reconstruction-based or prediction-based …

With the maturity of big data and computing power, deep learning has provided an end-to-
end efficient solution for fault diagnosis of rotating machinery. However, the diagnosis …

Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent
years. In particular, label shortage is a real challenge for the classification task on MTS …

Accurate PM 2.5 concentration prediction is essential and important for air pollution
assessment. The air pollutant concentration (eg PM 2.5 concentration) monitoring network …

Existing action recognition methods typically sample a few frames to represent each video to
avoid the enormous computation, which often limits the recognition performance. To tackle …

Multi-view clustering (MVC) exploits the information captured from diverse views to partition
data into different groups and attracts much attention recently. Despite significant progress …

Conventional time series classification approaches based on bags of patterns or shapelets
face significant challenges in dealing with a vast amount of feature candidates from high …