Graph element networks: Adaptive, structured computation and memory
Name
1904.09019.pdf
Description
Accepted version
Size
5.6 MB
Format
Unknown
Checksum (MD5)
d36f232189185996f742e8091cdb5769
Author(s)
Alet, Ferran
Jeewajee, Adarsh K
Bauza Villalonga, Maria
Rodriguez Garcia, Alberto
Lozano-Perez, Tomas
Kaelbling, Leslie P
Date Issued
2019
Journal
36th International Conference on Machine Learning, ICML 2019
Version
Author's final manuscript
Abstract
© 36th International Conference on Machine Learning, ICML 2019. All rights reserved. We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on the graph to model the relationship between an initial function defined over a space and a resulting function in the same space. We use GNNs as a computational substrate, and show that the locations of the nodes in space as well as their connectivity can be optimized to focus on the most complex parts of the space. Moreover, this representational strategy allows the learned input-output relationship to generalize over the size of the underlying space and run the same model at different levels of precision, trading computation for accuracy. We demonstrate this method on a traditional PDE problem, a physical prediction problem from robotics, and learning to predict scene images from novel viewpoints.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://proceedings.mlr.press/v97/alet19a.html
