[gov3009-l] 11/1 Applied Stats; Michael Bronstein

Dana Higgins danahiggins at fas.harvard.edu
Tue Oct 31 10:59:27 EDT 2017


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Michael
Bronstein*, Radcliffe Fellow at the Harvard Institute for Advanced Study.
He will be presenting work entitled *Deep Learning on Graphs: Going Beyond
Euclidean Data*.  Please find the abstract below and on the Applied Stats
website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.



As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided.  See you all there!

-- Dana Higgins




*Title:* *Deep Learning on Graphs: Going Beyond Euclidean Data*


*Abstract:*  In the past decade, deep learning methods have achieved
unprecedented performance on a broad range of problems in various fields
from computer vision to speech recognition. So far research has mainly
focused on developing deep learning methods for Euclidean-structured data.
However, many important applications have to deal with non-Euclidean
structured data, such as graphs and networks. Such data are becoming
increasingly important in computer graphics and 3D vision, sensor networks,
drug design, biomedicine, high energy physics, recommendation systems, and
web applications. The adoption of deep learning in these fields has been
lagging behind until recently, primarily since the non-Euclidean nature of
objects dealt with makes the very definition of basic operations used in
deep networks rather elusive. In this talk, I will introduce the emerging
field of geometric deep learning on graphs, overview existing solutions and
applications for this class of problems, and outline the key difficulties
and future research directions.
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