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Lecture 4: Equivariant CNNs I (Euclidean Spaces) - Maurice Weiler

Cristian Bodnar (11/7/23): A Sheaf-based Approach to Graph Neural Networks

Lecture 1: A Brief History of Geometric Deep Learning - Michael Bronstein

Lecture 2: Topological Message Passing - Cristian Bodnar

EPFL AI Center - A Physical perspective on Graph Neural Networks - Prof Michael Bronstein

Cats4AI talk: Sheaves for AI, Thomas Gebhart

Michael Bronstein - Geometric Deep Learning | MLSS Kraków 2023

Как происходит модернизация остаточных соединений [mHC]

Дэвид Джаз Майерс: «Три реализма и идея пучков»

ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein

Что такое теория категорий?

Lecture 5: Equivariant CNNs II (Riemannian manifolds) - Maurice Weiler

Michael Bronstein | Neural diffusion PDEs, differential geometry, and graph neural networks

Who Gives a Sheaf? Part 1: A First Example

ActInf MathStream 005.1 ~ Cristian Bodnar "Topological Deep Learning: Graphs, Complexes, Sheaves"

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Graph Neural Networks: A gentle introduction

Nonetheless one should learn the language of topos: Grothendieck... - Colin McLarty [2018]

Robert Ghrist (8/29/21): Laplacians and Network Sheaves

Geometric Deep Learning: GNNs Beyond Permutation Equivariance