3 citations · 8 across the 8 of their papers we have counts for
6 papers · 1 filter
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
Sukwon Yun, Inyoung Choi, Jie Peng +6
Multimodal learning has gained increasing importance across various fields, offering the ability to integrate data from diverse sources such as images, text, and personalized recor…
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Guibin Zhang, Haonan Dong, Yuchen Zhang +7
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset s…
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems
Guibin Zhang, Yanwei Yue, Zhixun Li +6
Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to…
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Guibin Zhang, Yanwei Yue, Xiangguo Sun +6
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, prima…
Graph Sparsification via Mixture of Graphs
Guibin Zhang, Xiangguo Sun, Yanwei Yue +4
Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale gr…
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Guibin Zhang, Yanwei Yue, Kun Wang +7
Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational challenges when applied to large-scale graphs. A promising solution is to remove non-essen…