Deep Learning for Robot Perception and Cognition

Chapter 4: Graph convolutional networks

Negar Heidari; Lukas Hedegaard; Alexandros Iosifidis    Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark

Abstract

Deep learning approaches have been very successful in many machine learning tasks including compute vision, natural language processing, audio processing, and speech recognition. However, deep neural networks typically work with grid-structured data represented in the Euclidean space and despite their recent successes, they poorly generalize to applications where the data is represented in non-Euclidean space. Recently, due to the increasing amount of graph structured data produced in different areas such as social networks, stock markets, and knowledge bases, ...