20 citations · 50 across the 9 of their papers we have counts for
9 papers
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
Hengchang Hu, Wei Guo, Yong Liu +1
In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse mo…
Can Large Language Models Empower Molecular Property Prediction?
Chen Qian, Huayi Tang, Zhirui Yang +2
Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines. Conventionally, a molecule graph can be repre…
Multi-Agent Cooperation via Unsupervised Learning of Joint Intentions
Shanqi Liu, Weiwei Liu, Wenzhou Chen +2
The field of cooperative multi-agent reinforcement learning (MARL) has seen widespread use in addressing complex coordination tasks. While value decomposition methods in MARL have…
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
Jun Chen, Shipeng Bai, Tianxin Huang +3
Neural network quantization is a very promising solution in the field of model compression, but its resulting accuracy highly depends on a training/fine-tuning process and requires…
Towards Understanding the Generalization of Graph Neural Networks
Huayi Tang, Yong Liu
Graph neural networks (GNNs) are the most widely adopted model in graph-structured data oriented learning and representation. Despite their extraordinary success in real-world appl…
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning
Shanqi Liu, Yujing Hu, Runze Wu +5
Real-world cooperation often requires intensive coordination among agents simultaneously. This task has been extensively studied within the framework of cooperative multi-agent rei…
A Quantitative Metric for Privacy Leakage in Federated Learning
Yong Liu, Xinghua Zhu, Jianzong Wang +1
In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. Ho…
Multi-Agent Game Abstraction via Graph Attention Neural Network
Yong Liu, Weixun Wang, Yujing Hu +3
In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process…
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
Weixun Wang, Tianpei Yang, Yong Liu +6
A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale…