Despite the visually-pleasing results achieved, the massive computational cost has been a
long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits …

Although deep neural networks (DNNs) have shown great successes in computer vision
tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the …

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 …

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

The time and effort required to manually design deep neural architectures is extremely high,
which has led to the development of neural architecture search technology as an automatic …

Deploying compute-intensive deep neural networks (DNNs) on resource-constrained end
devices has become a prominent trend, enabling localized intelligence. However, efficiently …

With the increasing number of new neural architecture designs and substantial existing
neural architectures, it becomes difficult for the researchers to situate their contributions …