Learning on Graphs
Speaker: Nitesh Chawla – Notre Dame, IN, United StatesTopic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Abstract
Graphs are ubiquitous across a variety of use-cases, and have emerged as a powerful means of representing complex systems. Graph Neural Networks have demonstrated exceptional effectiveness in handling graph data; however, there are numerous challenges from multiple data modalities to lack of labeled data. In this talk, I’ll introduce our work on learning from multiple data, and also the ideas of learning from limited data, including few-shot and self-supervised learning. I’ll also discuss applications of these methods.About this Lecture
Number of Slides: 45Duration: 60 minutes
Languages Available: English
Last Updated:
Request this Lecture
To request this particular lecture, please complete this online form.
Request a Tour
To request a tour with this speaker, please complete this online form.
All requests will be sent to ACM headquarters for review.