Learning multi-task communication with message passing
Pengfei Liu, Jie Fu*, Yue Dong*, Xipeng Qiu, Jackie Chi Kit Cheung
- We explore the problem of learning the relationship between multiple tasks and formulate this problem as message passing over a graph neural network.
- We borrow ideas from interaction systems and propose new architectures for multi-task learning Complete-graphs and Star-graphs .
- Different from traditional black-box learned models, this paper makes a step towards learning transferable and interpretable representations, which enables us to know what types of patterns are shared