Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

The sustainability of a clean energy transition for electric vehicle transportation is clearly
affected by increased energy consumption cost, which is associated with large-scale electric …

In the context of recent deep clustering studies, discriminative models dominate the literature
and report the most competitive performances. These models learn a deep discriminative …

The financial services sector has undergone a transformation due to predictive analytics and
data mining, which have improved risk assessment and strengthened fraud detection …

Abstract Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the edges …

For its robust predictive power (compared to pure physics-based models) and sample-
efficient training (compared to pure deep learning models), physics-informed deep learning …

Abstract Model quantization is a promising technique that can simultaneously compress and
accelerate a deep neural network by limiting its computation bit-width, which plays a crucial …

Segmentation via level-set methods plays a significant role in natural and medical image
analysis. Recently, the distance regularized level-set evolution (DRLSE) model, which is …

In order to meet the requirements for performance, safety, and latency in many IoT
applications, intelligent decisions must be made right here right now at the network edge …

The nonconvexity of the artificial neural network (ANN) training landscape brings
optimization difficulties. While the traditional back-propagation stochastic gradient descent …