6 accepted papers
Multi-domain graph pre-training has emerged as a pivotal technique in developing graph foundation models. While it greatly improves the generalization of graph neural networks, its privacy risks under membership inference attacks (MIAs), which aim to identify whether a specific instance was used in
Graph Foundation Models (GFMs) are pre-trained on diverse source domains and adapted to unseen targets, enabling broad generalization for graph machine learning. Despite that GFMs have attracted considerable attention recently, their vulnerability to backdoor attacks remains largely underexplored. A
Diffusion Transformers (DiTs) achieve strong video generation quality but suffer from high inference cost due to dense 3D attention, leading to the development of sparse attention technologies to improve efficiency. However, existing training-free sparse attention methods in video generation still f…
Robotic wrists play a crucial role in enhancing the dexterity and stability of robotic end-effectors. Existing rigid robotic wrists tend to be complex and lack flexibility, while soft robotic wrists often struggle with limited load-bearing capacity and lower accuracy. Human wrists feature multi-degr…
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates s…
Robotic wrists play a crucial role in enhancing the dexterity and stability of robotic end-effectors. Existing rigid robotic wrists tend to be complex and lack flexibility, while soft robotic wrists often struggle with limited load-bearing capacity and lower accuracy. Human wrists feature multi-degr