Web: http://arxiv.org/abs/2205.02767

May 6, 2022, 1:11 a.m. | Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, Siqiang Luo

cs.LG updates on arXiv.org arxiv.org

Graph Convolutional Networks (GCNs) achieve an impressive performance due to
the remarkable representation ability in learning the graph information.
However, GCNs, when implemented on a deep network, require expensive
computation power, making them difficult to be deployed on battery-powered
devices. In contrast, Spiking Neural Networks (SNNs), which perform a
bio-fidelity inference process, offer an energy-efficient neural architecture.
In this work, we propose SpikingGCN, an end-to-end framework that aims to
integrate the embedding of GCNs with the biofidelity characteristics of SNNs. …

arxiv graph networks

More from arxiv.org / cs.LG updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California