Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

To reduce the human efforts in neural network design, Neural Architecture Search (NAS)
has been applied with remarkable success to various high-level vision tasks such as …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by
generative adversarial networks (GAN). To compensate for the lack of training samples in …

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic
monitoring and driving assistant, deep learning-based object detection (DL-OD) has been …

Current dynamic networks and dynamic pruning methods have shown their promising
capability in reducing theoretical computation complexity. However, dynamic sparse …

Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking
architectures. Building a high-quality accuracy predictor usually costs enormous …

A myriad of recent breakthroughs in hand-crafted neural architectures for visual recognition
have highlighted the urgent need to explore hybrid architectures consisting of diversified …

Anomaly detection from graph data has drawn much attention due to its practical
significance in many critical applications including cybersecurity, finance, and social …