Many of the successes of machine learning are based on minimizing an averaged loss
function. However, it is well-known that this paradigm suffers from robustness issues that …

The development of next generation energy storage devices with low self‐discharge rate,
high energy density and low cost are the requirements to meet the future and environmental …

Various methods have been proposed to defend against adversarial attacks. However, there
is a lack of enough theoretical guarantee of the performance, thus leading to two problems …

Recent studies have investigated how to achieve robustness for unsupervised domain
adaptation (UDA). While most efforts focus on adversarial robustness, ie how the model …

Traffic congestion is a persistent problem in our society. Previous methods for traffic control
have proven futile in alleviating current congestion levels leading researchers to explore …

Despite significant advancements, collision-free navigation in autonomous driving is still
challenging, considering the navigation module needs to balance learning and planning to …

Urban intersections, filled with a diverse mix of vehicles from small cars to large semi-
trailers, present a persistent challenge for traffic control and management. This reality drives …

Traditional field test and laboratory test can only evaluate hardware performance, and
cannot test the robustness of artificial intelligence (AI) device for object detection, instance …

Abstract A Deep Neural Network (DNN)'s prediction may be unreliable outside of its training
distribution despite high levels of accuracy obtained during model training. The DNN may …

Driven by the need from real-world applications, Auxiliary Modality Learning (AML) offers the
possibility to utilize more information from auxiliary data in training, while only requiring data …