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individual search methods have been replaced by weight-sharing search methods for higher …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

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) enables researchers to automatically explore broad
design spaces in order to improve efficiency of neural networks. This efficiency is especially …

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 …

Early methods in the rapidly developing field of neural architecture search (NAS) required
fully training thousands of neural networks. To reduce this extreme computational cost …

Neural architecture search (NAS) adopts a search strategy to explore the predefined search
space to find superior architecture with the minimum searching costs. Bayesian optimization …

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 …

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum
advantages for practical problems under the quantum–classical hybrid computational …

Diffusion probabilistic models (DPMs) are a new class of generative models that have
achieved state-of-the-art generation quality in various domains. Despite the promise, one …