Code for the website of the NeurIPS 2020 workshop on 'Topological Data Analysis and Beyond'
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Updated
Feb 11, 2021 - TeX
Code for the website of the NeurIPS 2020 workshop on 'Topological Data Analysis and Beyond'
Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.
DONUT: Database of Original and Non-Theoretical Applications of Topology
LaTeX source code of the paper and poster of the paper Simplicial Neural Networks
Lecture notes for Topological Data Analysis.
Persistent Homology as Stopping-Criterion for Voronoi Interpolation.
Topological Data Analysis on a Brain Network
Moja prezentacja o topologicznej analizie danych (chociaż jest bardziej o topologii niż o danych)
Final project for MATH-478 of Spring 2021
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