Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by
updating only a few parameters so as to improve storage efficiency, called parameter …

Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been
widely studied for deep neural network (DNN) model compression, especially for recurrent …

Latest development of neural models has connected the encoder and decoder through a
self-attention mechanism. In particular, Transformer, which is solely based on self-attention …

Learning from spatio-temporal data has numerous applications such as human-behavior
analysis, object tracking, video compression, and physics simulation. However, existing …

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-
world applications. In order to obtain performance gains, these networks have grown larger …

In the modern tourism industry, next point-of-interest (POI) recommendation is an important
mobile service as it effectively aids hesitating travelers to decide the next POI to visit …

Cyber–physical–social systems (CPSS), an emerging cross-disciplinary research area,
combines cyber–physical systems (CPS) with social networking for the purpose of providing …