Conference proceeding
Braid Manifold Discovery using Temporal Graph Networks
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings
01/01/2022
DOI: 10.1109/OCEANS47191.2022.9977338
Abstract
Conference Title: OCEANS 2022, Hampton Roads Conference Start Date: 2022, Oct. 17 Conference End Date: 2022, Oct. 20 Conference Location: Hampton Roads, VA, USAThis paper presents initial findings utilizing Graph Neural Networks (GNNs) to perform classification on experimental active sonar data. GNNs enable using neural networks on graphs, which were previously difficult to train on due to the permutation invariant property of graphs. Nodes in a graph are formed by thresholding sonar ping spectrograms. Edges in a graph are formed by first calculating the correlation of the remaining node values across multiple ping spectrograms, and then assigning edges between two nodes if the correlation value exceeds a defined threshold. Both the graph’s adjacency matrix and the graph’s node embeddings are then used as input into the GNN. We use a variant of GNNs called Temporal Graph Networks to allow learning on graphs that change over time.
Details
- Title: Subtitle
- Braid Manifold Discovery using Temporal Graph Networks
- Creators
- Ananya Sen GuptaIvars Kirsteins
- Resource Type
- Conference proceeding
- Publication Details
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings
- DOI
- 10.1109/OCEANS47191.2022.9977338
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Grant note
- DOI: 10.13039/100000006, name: Office of Naval Research
- Language
- English
- Date published
- 01/01/2022
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984353646602771
Metrics
31 Record Views