Mathilde Papillon

will give a presentation on :

Topological Learning

Abstract:

The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a comprehensive framework to process and extract knowledge from data associated with these systems. TDL has demonstrated theoretical and practical advantages that hold the promise of breaking ground in the applied sciences and beyond. However, the rapid growth of the TDL literature for relational systems has also led to a lack of unification in notation and language across message-passing architectures. This presents a real obstacle to building upon existing works and deploying TNNs to new real-world problems. In this talk, I provide a turn-key, unified mathematical and graphical notation for TDL and apply it to the existing body of literature. I will shed light on current open questions in the field and exciting opportunities for future development.

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