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 …

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individual search methods have been replaced by weight-sharing search methods for higher …

Over the past half-decade, many methods have been considered for neural architecture
search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter …

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 …

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

Neural architecture search (NAS) has been extensively studied in the past few years. A
popular approach is to represent each neural architecture in the search space as a directed …

An effective and efficient architecture performance evaluation scheme is essential for the
success of Neural Architecture Search (NAS). To save computational cost, most of existing …

The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has
significantly lowered the computational overhead for conducting scientific research in neural …

Deep learning plays a critical role in medical image segmentation. Nevertheless, manually
designing a neural network for a specific segmentation problem is a very difficult and time …

Transformer-based pre-trained models have revolutionized NLP for superior performance
and generality. Fine-tuning pre-trained models for downstream tasks often requires private …