YoVDO

Learning Distances for Attributed Graphs with Optimal Transport

Offered By: IEEE Signal Processing Society via YouTube

Tags

Graph Theory Courses Data Science Courses Signal Processing Courses Optimal Transport Courses

Course Description

Overview

Explore the concept of learning distances for attributed graphs using optimal transport in this comprehensive webinar presented by Pierre Borgnat from CNRS. Gain insights into this advanced topic as part of the Data sciEnce on GrAphS (DEGAS) Webinar Series, organized in collaboration with the IEEE Signal Processing Society Data Science Initiative. Delve into the intricacies of graph theory and data science, and discover how optimal transport techniques can be applied to measure distances between attributed graphs. Enhance your understanding of this cutting-edge research area and its potential applications in signal processing and data analysis.

Syllabus

Learning distances for attributed graphs with optimal transport


Taught by

IEEE Signal Processing Society

Related Courses

Optimal Transport and PDE - Gradient Flows in the Wasserstein Metric
Simons Institute via YouTube Crash Course on Optimal Transport
Simons Institute via YouTube Learning From Ranks, Learning to Rank - Jean-Philippe Vert, Google Brain
Alan Turing Institute via YouTube Optimal Transport for Machine Learning - Gabriel Peyre, Ecole Normale Superieure
Alan Turing Institute via YouTube Regularization for Optimal Transport and Dynamic Time Warping Distances - Marco Cuturi
Alan Turing Institute via YouTube