Foundations of Invariant and Equivariant Representation Learning

Sponsors: NSF CAREER:#1943364 and The Wabash Heartland Innovation Network

Scroll below for tutorials and datasets.


G-invariant-networks

G-Invariant & G-Equivariant Neural Networks:
A complete beginner's guide with step-by-step examples

In this step-by-step tutorial we introduce the mathematical foundations of the most common type of group-invariant and group-equivariant representation learning. Examples include learning invariant representations of graphs and images.

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point-clouds

Sets and Point Cloud Representation Learning:
A complete beginner's guide with step-by-step examples

In this step-by-step tutorial we introduce the mathematical foundations of the most common type of set representations, including representations of point clouds through Lie G-invariant & G-equivariant representations.

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GNNs SVDs

Graph Representation Learning:
A complete beginner's guide with step-by-step examples

In this step-by-step tutorial we introduce the mathematical foundations of graph and knowledge graph representations, including the connection between graph neural networks (GNNs) and eigenvectors.

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