Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human
performance across various domains. This presents us with an opportunity to further human …

The focus of recent research has shifted from merely increasing the Deep Neural Networks
(DNNs) performance in various tasks to DNNs, which are more interpretable to humans. The …

The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very
challenging due to the difficulty of expressing formal specifications for vision tasks and the …

Abstract Concept-based models perform prediction using a set of concepts that are
interpretable to stakeholders. However, such models often involve a fixed, large number of …

Pest management is an important factor affecting agricultural and food industry products. A
large number of insect species and the subtle differences bring a challenge to the accurate …

Deep neural networks often rely on spurious features, which are attributes correlated with
class labels but irrelevant to the actual task, leading to poor generalization when these …

Abstract One-shot Network Pruning at Initialization (OPaI) is acknowledged as a highly cost-
effective strategy for network pruning. However, it has been observed that OPaI models tend …

The trade-off between expressiveness and interpretability remains a core challenge when
building human-centric predictive models for classification and decision-making. While …

Concept activation vector (CAV) has attracted broad research interest in explainable AI, by
elegantly attributing model predictions to specific concepts. However, the training of CAV …

Deep generative models like VAEs and diffusion models have advanced various generation
tasks by leveraging latent variables to learn data distributions and generate high-quality …