Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

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 …

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their
low computational budgets constrain both the depth (number of convolution layers) and the …

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

Differentiable architecture search (DARTS) provided a fast solution in finding effective
network architectures, but suffered from large memory and computing overheads in jointly …

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …

The time and effort involved in hand-designing deep neural networks is immense. This has
prompted the development of Neural Architecture Search (NAS) techniques to automate this …