Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed
itself as the trending approach to automate the discovery of deep neural network …

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 …

Abstract Neural Architecture Search (NAS) has attracted increasingly more attention in
recent years because of its capability to design deep neural network automatically. Among …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

We investigate the potential of GPT-4~\cite {gpt4} to perform Neural Architecture Search
(NAS)--the task of designing effective neural architectures. Our proposed approach,\textbf …

Despite its flexibility to learn diverse inductive biases in machine learning programs, meta
learning (ie,\learning to learn) has long been recognized to suffer from poor scalability due …

Anomaly detection on graphs plays a significant role in various domains, including
cybersecurity, e-commerce, and financial fraud detection. However, existing methods on …

Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning
problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve …

In this paper, we propose a Shapley value based method to evaluate operation contribution
(Shapley-NAS) for neural architecture search. Differentiable architecture search (DARTS) …