Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

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 …

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

Given the recent impressive accomplishments of language models (LMs) for code
generation, we explore the use of LMs as general adaptive mutation and crossover …

Head pose estimation suffers from several problems, including low pose tolerance under
different disturbances and ambiguity arising from common head pose representation. In this …

Neural Architecture Search (NAS) is quickly becoming the standard methodology to design
neural network models. However, NAS is typically compute-intensive because multiple …

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …

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 …

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of
top-performer neural networks. Current works require heavy training of supernet or intensive …