Deep learning with graph-structured representations

Abstract

In this thesis, we propose novel approaches to machine learning with structured data. Our proposed methods are largely based on the theme of structuring the representations and computations of neural network-based models in the form of a graph, which allows for improved generalization when learning from data with both explicit and implicit modular structure