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 …

Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …

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 …

Neural architecture search (NAS) has achieved breakthrough success in a great number of
applications in the past few years. It could be time to take a step back and analyze the good …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Conventional neural architecture search (NAS) approaches are usually based on
reinforcement learning or evolutionary strategy, which take more than 1000 GPU hours to …

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 …

This paper addresses the scalability challenge of architecture search by formulating the task
in a differentiable manner. Unlike conventional approaches of applying evolution or …

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 …