Geometric and Topological deep learning is a project that is aimed at two goals

(1) Introducing topological language to deep learning for the purpose of utilizing the minimal mathematical structures to formalize problems that arise in a generic deep learning problem.
(2) Create, augment and enhance novel deep learning models utilizing the tools available in topology and geometry.

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Cell Complex Neural Networks, joint work with Kyle Istvan and Ghada Zamzmi. NeurIPS 2020 Workshop on Topological Data Analysis and Beyond.

 

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A Topological Framework for Deep Learning. Joint work with Kyle Istvan. Under Review.

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Simplicial Complex Representation Learning, Joint work with Ghada Zamzmi, and Xuanting Cai