Evolutionary neural architecture search (ENAS) and differentiable architecture search
(DARTS) are all prominent algorithms in neural architecture search, enabling the automated …

Abstract Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the
design of deep learning architectures tailored specifically to a given target hardware …

Abstract Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to
automatically design the architecture of a neural network for a specific task and target …

Convolutional neural networks (CNNs) are popularly-used in various AI fields, yet the design
of CNN architectures heavily depends on domain expertise. Evolutionary neural architecture …

Abstract Neural Architecture Search (NAS) is crucial in the field of sustainable computing as
it facilitates the development of highly efficient and effective neural networks. However, it …

Neural architecture search (NAS) has shown promise towards automating neural network
design for a given task, but it is computationally demanding due to training costs associated …

Neural Architecture Search (NAS) aims to automate the discovery of high-performing deep
neural network architectures. Traditional objective-based NAS approaches typically optimize …

The evolutionary paradigm has been successfully applied to neural network search (NAS) in
recent years. Due to the vast search complexity of the global space, current research mainly …

Neural architecture search (NAS) aims to automate neural network design process and has
shown promising results for image classification tasks. Owing to combinatorially huge neural …

An efficient convolution kernel could transform images into more expressive representations,
which usually determines the quality of image classification models. Algorithms in the field of …