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 …

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

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

The ever-growing demand and complexity of machine learning are putting pressure on
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …

The design of neural networks typically involves trial-and-error, a time-consuming process
for obtaining an optimal architecture, even for experienced researchers. Additionally, it is …

Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand
innovative neural architecture designs, particularly within the constrained environments of …

This paper studies the challenging black-box adversarial attack that aims to generate
adversarial examples against a black-box model by only using output feedback of the model …

Abstract Neural Architecture Search (NAS) methods seek effective optimization toward
performance metrics regarding model accuracy and generalization while facing challenges …

Recently zero-cost proxies for neural architecture search (NAS) have attracted increasing
attention. They allow us to discover top-performing neural networks through architecture …

The development of Neural Architecture Search (NAS) is hindered by high costs associated
with evaluating network architectures. Recently, several zero-cost proxies have been …