Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

by   Wenlin Wang, et al.
Duke University

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.


SStaGCN: Simplified stacking based graph convolutional networks

Graph convolutional network (GCN) is a powerful model studied broadly in...

Graphite: Iterative Generative Modeling of Graphs

Graphs are a fundamental abstraction for modeling relational data. Howev...

Generative Graph Convolutional Network for Growing Graphs

Modeling generative process of growing graphs has wide applications in s...

Generative Modeling for Multi-task Visual Learning

Generative modeling has recently shown great promise in computer vision,...

Multi-view Graph Convolutional Networks with Differentiable Node Selection

Multi-view data containing complementary and consensus information can f...

Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks

Coherence is an important aspect of text quality, and various approaches...

Learning an Interpretable Graph Structure in Multi-Task Learning

We present a novel methodology to jointly perform multi-task learning an...

Please sign up or login with your details

Forgot password? Click here to reset