The last few years have witnessed impressive success of deep learning in many heterogeneous fields: image recognition, games, biology, natural language processing, to name a few. However, we are still a long way from truly understanding the mathematics behind these accomplishments. Beside being of a theoretical interest by itself, understanding why these techniques work so well would certainly both increase their performance and expand their fields of application.

Geometric deep learning (GDL) is a very promising field targeting such a mathematical understanding. GDL proposes a unified approach to understand why diverse architectures, such as CNN, LSTM, Graph Neural Networks, Transformers, are so successful. The powerful underlying idea is that whenever the function to be approximated is invariant/equivariant by a group G of symmetries, G-invariance/equivariance should be encoded in the architecture. For instance, this is why CNNs, that implement a translational equivariance by weight sharing, work so well on image recognition, which is a naturally translation-equivariant task.

The First Italian School in Geometric Deep Learning is the first GDL intensive school located in Italy. This will foster a GDL community in Italy, with links to other European countries, and ultimately boost research in GDL.

A targeted, intensive course is the ideal setting to enable students to tackle GDL, a field that would otherwise require separate training in several diverse skills, ranging from group theory and differential geometry, to graph theory, probability and deep learning.

The intended audience includes graduate students in Computer Science, Mathematics, Physics and Statistics, as well as postdoctoral fellows and young researchers. Any quantitative-oriented student is welcome, and a list of prerequisites will be provided.

Where, When and hoW

• The school will take place in person at the University campus of Pescara, Italy, from 25th to 28th of July, 2022.
• Register here. The deadline for registration is 15th of June, but for organizing reasons we ask you to register as soon as possible.
• There is a registration fee of 100€ that can be paid only onsite, and no payment is required at registration time.
• Limited funding is available to partially support selected students. Please ask for support when registering.

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