12,608 research outputs found
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple
heterogeneous learning tasks into the same framework. The proposed model is
based on the fact that heterogeneous learning tasks, which correspond to
different generative processes, often rely on data with a shared graph
structure. Accordingly, our model combines a graph convolutional network (GCN)
with multiple variational autoencoders, thus embedding the nodes of the graph
i.e., samples for the tasks) in a uniform manner while specializing their
organization and usage to different tasks. With a focus on healthcare
applications (tasks), including clinical topic modeling, procedure
recommendation and admission-type prediction, we demonstrate that our method
successfully leverages information across different tasks, boosting performance
in all tasks and outperforming existing state-of-the-art approaches.Comment: Accepted by AAAI-202
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