Have you ever wondered how powerful graph neural networks are? Interested in learning methods that automatically learn to encode graph structure into low-dimensional embeddings? If so, this colloquium talk is the one for you.
Jure Leskovec is our LTI colloquium guest speaker for this week, 4/05/19.
Where: Doherty Hall 2315
When: 2:30-3:50 pm
When: Friday, April 5th, 2019
HOW POWERFUL ARE GRAPH NEURAL NETWORKS?
Abstract:
Machine learning
on graphs is an important and ubiquitous task with applications ranging from
drug design to friendship recommendation in social networks. The primary
challenge in this domain is finding a way to represent, or encode, graph
structure so that it can be easily exploited by machine learning models.
However, traditionally machine learning approaches relied on user-defined
heuristics to extract features encoding structural information about a graph.
In this
talk I will discuss methods that automatically learn to encode graph structure
into low-dimensional embeddings,
using techniques based on deep learning and nonlinear dimensionality reduction.
I will provide a conceptual review of key advancements in this area of
representation learning on graphs, including graph convolutional networks and
their representational power. We will also discuss applications to web-scale
recommender systems, healthcare, and knowledge representation and reasoning.
BIO:
Jure Leskovec <http://cs.stanford.edu/~jure>
is Associate Professor of Computer Science at Stanford University, Chief
Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His
research focuses on machine learning and data mining large social, information,
and biological networks, their evolution, and the diffusion of information over
them.
Computation over
massive data is at the heart of his research and has applications in computer
science, social sciences, marketing, and biomedicine. This research has won
several awards including a Lagrange Prize, Microsoft Research Faculty
Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper
awards. Leskovec received
his bachelor's degree in computer science from University of Ljubljana,
Slovenia, and his PhD
in
machine learning from the Carnegie Mellon University and postdoctoral training
at Cornell University.
