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

Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …

The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so
that research on out-of-distribution (OOD) generalization comes into the spotlight …

Vision transformers (ViTs) have recently received explosive popularity, but their enormous
model sizes and training costs remain daunting. Conventional post-training pruning often …

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 …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

The computer vision world has been re-gaining enthusiasm in various pre-trained models,
including both classical ImageNet supervised pre-training and recently emerged self …