Upcoming Webinar: 28 April 2021 by Dr. Fernando Gama

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Upcoming Webinar: 28 April 2021 by Dr. Fernando Gama

Upcoming Webinar -  28 April 2021
Webinar Topic: "Graph Neural Networks"

Presenter: Dr. Fernando Gama

Based on the IEEE Xplore® article
Convolutional Neural Network Architectures for Signals Supported on Graphs
published in the
IEEE Transactions on Signal Processing, December 2018

 

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Dr. Fernando Gama  
28 April 2021
1:00 PM ET (New York time)
Approximately 1 hour
Attendee Registration

Original article will be made freely available for download on the day of the webinar, on IEEE Xplore®

 

Register for the Webinar

 

The IEEE Signal Processing Society would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic. We appreciate your continued patience and support as we work together to navigate these unforeseen and uncertain circumstances. We hope that you, your families, and your communities are safe!

About this topic:

Graphs are generic models of signal structure that can help to learn in several practical problems. To learn from graph data, we need scalable architectures that can be trained on moderate dataset sizes and that can be implemented in a distributed manner. Drawing from graph signal processing, the webinar will define graph convolutions and use them to introduce graph neural networks (GNNs). It will prove that GNNs are permutation equivariant and stable to perturbations of the graph, properties that explain their scalability and transferability. These results help understand the advantages of GNNs over linear graph filters. Introducing the problem of learning decentralized controllers. This webinar will further discuss how GNNs naturally leverage the partial information structure inherent to distributed systems in order to learn useful efficient controllers. Using flocking as an illustrative example, the presenter will show that GNNs, can not only successfully learn distributed actions that coordinate the team, but also transfer and scale to larger teams.


About the presenters:

Zhiguo Ding

Dr. Fernando Gama (SM'14, M'21) received the electronic engineer degree from the University of Buenos Aires, Argentina, in 2013, the M.A. degree in statistics from the Wharton School, University of Pennsylvania, in 2017, and the Ph.D. degree in electrical and systems engineering from the University of Pennsylvania, Philadelphia, PA, in 2020.

He has been a visiting researcher at Delft University of Technology (TU Delft), Netherlands, in 2017 and a research intern at Facebook Artificial Intelligence Research, Montreal, Canada, in 2018. He is currently a postdoctoral scholar in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, CA.

Dr. Gama has been awarded a Fulbright scholarship for international students and he has received a best student paper award at EUSIPCO 2019.

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