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Geometric Deep Learning

Geometric Deep Learning (GDL) is concerned with designing and analyzing neural networks for unstructured sets, grids (2D, 3D), graphs, and manifolds. They can be understood in a unified manner as methods that respect the structure and symmetries of these domains. GDL techniques are at the core of many recent breakthroughs from discovery of new drugs to improved traffic forecasting. For a short introduction to the topic I recommend this keynote talk.


The goal of the proseminar is to teach you how to deliver a scientific presentation. You study a given topic, summarize it, and present it to the other participants.

First, we will provide advice and resources that will help you prepare and deliver a scientific presentation. Then, after you are assigned to an individual topic, you will give an initial practice presentation and receive detailed feedback from all participants. You will have the chance to incorporate this feedback before giving a final graded presentation. At the end of the semester you also need to submit a 2-page summary.

In the first part of the semester, we will have practice presentations for 2-3 topics each week. The second part of the semester will consist of two block sessions where participants will give their final presentations. Attendance is mandatory and the meetings will be virtual or hybrid. The exact meeting dates will be determined in the kick-off meeting.

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