6 papers
EO-1: An Open Unified Embodied Foundation Model for General Robot Control
Delin Qu, Haoming Song, Qizhi Chen +13
The human ability to seamlessly perform multimodal reasoning and physical interaction in the open world is a core goal for general purpose embodied intelligent systems. Recent visi…
TR-PTS: Task-Relevant Parameter and Token Selection for Efficient Tuning
Siqi Luo, Haoran Yang, Yi Xin +4
Large pre-trained models achieve remarkable performance in vision tasks but are impractical for fine-tuning due to high computational and storage costs. Parameter-Efficient Fine-Tu…
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks
Yanwei Yue, Guibin Zhang, Haoran Yang +1
Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when…
IENE: Identifying and Extrapolating the Node Environment for Out-of-Distribution Generalization on Graphs
Haoran Yang, Xiaobing Pei, Kai Yuan
Due to the performance degradation of graph neural networks (GNNs) under distribution shifts, the work on out-of-distribution (OOD) generalization on graphs has received widespread…
Hyperedge Interaction-aware Hypergraph Neural Network
Rongping Ye, Xiaobing Pei, Haoran Yang +1
Hypergraphs provide an effective modeling approach for modeling high-order relationships in many real-world datasets. To capture such complex relationships, several hypergraph neur…
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs
Xiaobing Pei, Haoran Yang, Gang Shen
Recent studies have shown that attackers can catastrophically reduce the performance of GNNs by maliciously modifying the graph structure or node features on the graph. Adversarial…