Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Deep Learning has enabled remarkable progress over the last years on a variety of tasks,
such as image recognition, speech recognition, and machine translation. One crucial aspect …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

The convolutional neural network (CNN), which is one of the deep learning models, has
seen much success in a variety of computer vision tasks. However, designing CNN …

In this paper, we study design of deep neural networks for tasks of image restoration. We
propose a novel style of residual connections dubbed" dual residual connection", which …

Neural architecture search (NAS) has shown great promise in designing state-of-the-art
(SOTA) models that are both accurate and efficient. Recently, two-stage NAS, eg BigNAS …

A variety of methods have been applied to the architectural configuration and learning or
training of artificial deep neural networks (DNN). These methods play a crucial role in the …

Finding a well-performing architecture is often tedious for both deep learning practitioners
and researchers, leading to tremendous interest in the automation of this task by means of …

Although remarkable progress has been made on single image super-resolution due to the
revival of deep convolutional neural networks, deep learning methods are confronted with …