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 …

Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …

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

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 …

We propose Stochastic Neural Architecture Search (SNAS), an economical end-to-end
solution to Neural Architecture Search (NAS) that trains neural operation parameters and …

Abstract We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive
approach for automatic model design. ENAS constructs a large computational graph, where …

Automatic neural architecture design has shown its potential in discovering powerful neural
network architectures. Existing methods, no matter based on reinforcement learning or …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

Over the past half-decade, many methods have been considered for neural architecture
search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter …

ABSTRACT Neural Architecture Search (NAS) is a logical next step in the automatic learning
of representations, but the development of NAS methods is slowed by high computational …