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individual search methods have been replaced by weight-sharing search methods for higher …

The recent emergence of deep learning to characterize complex patterns of protein big data
reveals its potential to address the classic challenges in the field of protein data mining …

Neural Architecture Search (NAS) is an exciting new field which promises to be as much as
a game-changer as Convolutional Neural Networks were in 2012. Despite many great works …

Efficient neural architecture search (ENAS) achieves novel efficiency for learning
architecture with high-performance via parameter sharing and reinforcement learning (RL) …

Abstract Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art
performance through the discovery of new architecture patterns, without human intervention …

Neural Architecture Search methods have been successfully applied to image tasks with
excellent results. However, NAS methods are often complex and tend to quickly converge for …

Abstract Neural Architecture Search (NAS) benchmarks significantly improved the capability
of developing and comparing NAS methods while at the same time drastically reduced the …

Quantization for model compression can efficiently reduce the network complexity and
storage requirement but the original training data is necessary to remedy the performance …

Abstract Neural Architecture Search (NAS) is a collection of methods to craft the way neural
networks are built. We apply this idea to Federated Learning (FL), wherein predefined …

Networks found with neural architecture search (NAS) achieve the state-of-the-art
performance in a variety of tasks, out-performing human-designed networks. However, most …