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Oral
Graph Element Networks: adaptive, structured computation and memory
Ferran Alet · Adarsh Keshav Jeewajee · Maria Bauza Villalonga · Alberto Rodriguez · Tomas Lozano-Perez · Leslie Kaelbling

Wed Jun 12 02:40 PM -- 03:00 PM (PDT) @ Hall A
in Deep Learning Architectures »

We explore the use of graph-structured neural-networks (GNNs) to model spatial processes in which there is no {\em a priori} graphical structure. Similar to {\em 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. The encoding of inputs to node states, the decoding of node states to outputs, as well as the mappings defining the GNN are learned from a training set consisting of data from multiple function pairs. The locations of the nodes in space as well as their connectivity can be adjusted during the training process. This graph-based representational strategy allows the learned input-output relationship to generalize over the size and even topology of the underlying space. We demonstrate this method on a traditional PDE problem, a physical prediction problem from robotics, and a problem of learning to predict scene images from novel viewpoints.

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Author Information

Ferran Alet (MIT)
Adarsh Keshav Jeewajee (Massachusetts Institute of Technology)
Maria Bauza Villalonga (MIT)
Alberto Rodriguez (MIT)
Tomas Lozano-Perez (MIT)
Leslie Kaelbling ((organization))

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