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Factor Graph Neural Networks
Zhen Zhang · Fan Wu · Wee Sun Lee

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1434

Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks (GNNs) have been successfully applied to graph-structured data such as point cloud and molecular data. These networks often only consider pairwise dependencies, as they operate on a graph structure. We generalize the GNN into a factor graph neural network (FGNN) providing a simple way to incorporate dependencies among multiple variables. We show that FGNN is able to represent Max-Product belief propagation, an approximate inference method on probabilistic graphical models, providing a theoretical understanding on the capabilities of FGNN and related GNNs. Experiments on synthetic and real datasets demonstrate the potential of the proposed architecture.

Author Information

Zhen Zhang (University of Adelaide)
Fan Wu (University of Illinois at Urbana-Champaign)
Wee Sun Lee (National University of Singapore)

Wee Sun Lee is a professor in the Department of Computer Science, National University of Singapore. He obtained his B.Eng from the University of Queensland in 1992 and his Ph.D. from the Australian National University in 1996. He has been a research fellow at the Australian Defence Force Academy, a fellow of the Singapore-MIT Alliance, and a visiting scientist at MIT. His research interests include machine learning, planning under uncertainty, and approximate inference. His works have won the Test of Time Award at Robotics: Science and Systems (RSS) 2021, the RoboCup Best Paper Award at International Conference on Intelligent Robots and Systems (IROS) 2015, the Google Best Student Paper Award, Uncertainty in AI (UAI) 2014 (as faculty co-author), as well as several competitions and challenges. He has been an area chair for machine learning and AI conferences such as the Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), the AAAI Conference on Artificial Intelligence (AAAI), and the International Joint Conference on Artificial Intelligence (IJCAI). He was a program, conference and journal track co-chair for the Asian Conference on Machine Learning (ACML), and he is currently the co-chair of the steering committee of ACML.

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