Abstract Mixed-Precision Quantization (MQ) can achieve a competitive accuracy-complexity
trade-off for models. Conventional training-based search methods require time-consuming …

Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique
aiming to significantly speed up algorithms for neural architecture search (NAS). Recent …

The goal of neural architecture search (NAS) is to either downsize the neural architecture
and model of a deep neural network (DNN), adjust a neural architecture to improve its end …

Conducting efficient performance estimations of neural architectures is a major challenge in
neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot …

Neural Architecture Search (NAS) is widely used to automatically obtain the neural network
with the best performance among a large number of candidate architectures. To reduce the …

Abstract The Neural Architecture Search (NAS) problem is typically formulated as a graph
search problem where the goal is to learn the optimal operations over edges in order to …

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 …

In the last decade, most research in Machine Learning contributed to the improvement of
existing models, with the aim of increasing the performance of neural networks for the …

Neural architecture search (NAS) has made tremendous progress in the automatic design of
effective neural network structures but suffers from a heavy computational burden. One-shot …

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