Juergen Jöst: Geometric principles of data visualization @ToposInstitute
Juergen Jöst: Geometric principles of data visualization  @ToposInstitute
Uploaded March 2025 | Updated September 2026, 2 weeks ago
Topos Institute Colloquium, 27th of March 2025.
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Many machine learning algorithms try to visualize high dimensional metric data in 2D in such a way that the essential geometric and topological features of the data are highlighted. Recent schemes like the popular UMAP depend on sophisticated mathematical tools from Riemannian geometry and category theory. We develop and improve the mathematical theory in a rigorous way, leading to an improved algorithm, IsUMap. The category theoretical foundations include Spivak's fuzzy simplicial sets, and they also offer a link to Topological Data Analysis. They provide a conceptual tool for merging different generalized metric structures, and we will also put them in a wider context.
Juergen Jöst: Geometric principles of data visualizationBerkeley Seminar: David Jaz Myers, 8/7/2023[Berkeley Seminar] Brendan Fong | Processes of Production of AbstractionAlex Kavvos: Two-dimensional Kripke Semantics[Oxford Seminar] David Jaz Myers | Compositionality via 2-algebraAstra Kolomatskaia: Towards higher-dimensional syntax[DOTS Lectures] 8. Compositionality of behaviours: generalized Moore machine caseThe Joy of Abstraction book club — Chapter 5[Oxford Seminar] Tim Hosgood | Homotopy coherent Bousfield–KanStefan Milius: Demystifying Codensity Monads via Duality[Berkeley Seminar] Mike Dodds (Galois) | What works and doesnt selling formal methods in industryAleks Kissinger: ZX Calculus and Fault-tolerant quantum computing
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Juergen Jöst: "Geometric principles of data visualization"

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