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Differentiable Mapper for Topological Optimization of Data Representation

Now accepted at ICML 2024!

The Mapper algorithm has been known to be a powerful tool in visualization and data analytics, but suffers from dependence on many hand-turning parameters. In this work, we propose a relaxation and generalization of the Mapper so that the parameters can be optimized using gradient descent. Convergence results and applications are also provided.

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Deep Mapper: Efficient Visualization of Plausible Conformational Pathways

Joint work with Fujitsu Ltd. - Now accepted at ECAI 2024!

We introduce a novel variant of the Mapper algorithm in order to find plausible protein conformational pathways. When applied to a 50S-ribosomal dataset, this proves to be more efficient than existing methods, while successfully recovering well-recognized pathways.

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Differentiable Mapper

Published: May 01, 2024

Slides can be found here.