Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Deployment of modern data-driven machine learning methods, most often realized by deep
neural networks (DNNs), in safety-critical applications such as health care, industrial plant …

It is difficult to obtain expensive labeled data in industrial fault diagnosis applications, which
easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this …

Driving behaviour understanding is of vital importance in improving transportation safety and
promoting the development of Intelligent Transportation Systems (ITS). As a long-standing …

Although fine-tuning pre-trained language models (PLMs) renders strong performance in
many NLP tasks, it relies on excessive labeled data. Recently, researchers have resorted to …

Advanced Hyperspectral image sensors can capture high-resolution land cover images.
Many supervised Machine learning (ML) and Deep learning (DL) algorithms succeeded in …

Human activity recognition (HAR), which aims at inferring the behavioral patterns of people,
is a fundamental research problem in digital health and ambient intelligence. The …

When deploying deep learning technology in self-driving cars, deep neural networks are
constantly exposed to domain shifts. These include, eg, changes in weather conditions, time …

The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …