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Geometric Deep Learning

Description

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. However, 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 manifolds. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, 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. The purpose of the course is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications, as well as key challenges and future directions.

 

PREREQUISITES
Machine Learning

 

RECOMMENDED COURSES
Computer Vision & Pattern Recognition

 

 

REFERENCES

  • M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, "Geometric deep learning: going beyond Euclidean data", IEEE Signal Processing Magazine, Vol. 34(4), pp. 18-42, 2017

People

 

Bronstein M.

Course director

Monti F.

Assistant

Additional information

Semester
Spring
Academic year
2018-2019
ECTS
3
Language
English
Education
Master of Science in Artificial Intelligence, Core course, Lecture, 2nd year

Master of Science in Computational Science, Elective course, Lecture, 2nd year

Master of Science in Informatics, Elective course, Lecture, 2nd year

Master of Science in Informatics, Elective course, Lecture, 1st year

PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (2 ECTS)

PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (2 ECTS)