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 …

In recent years, deep learning (DL) has been widely studied using various methods across
the globe, especially with respect to training methods and network structures, proving highly …

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 …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Neural Architecture Search (NAS), a promising and fast-moving research field, aims to
automate the architectural design of Deep Neural Networks (DNNs) to achieve better …

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 …

Despite the impressive progress in neural network architecture design, improving the
performance of the existing state-of-the-art models has become increasingly challenging …

Neural Architecture Search (NAS) is a research field concerned with utilizing optimization
algorithms to design optimal neural network architectures. There are many approaches …

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 …