---
title: "Bastian Rieck"
type: "entity"
entity_kind: "person"
status: "hub"
canonical_name: "Bastian Rieck"
aliases: []
first_seen: "2026-07-21T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["Neural Persistence","persistent homology","Karsten Borgwardt","topological data analysis","Tolga Birdal"]
seek_code_commit: "850b81d"
---


First author of "Neural Persistence: A Complexity Measure for Deep Neural
Networks Using Algebraic Topology" (with Matteo Togninalli, Christian Bock,
Michael Moor, Max Horn, Thomas Gumbsch & Karsten Borgwardt; ICLR 2019), a
method that scores a trained network's complexity by running persistent
homology directly on its weights. Matters to the vault as the third
founding-era neural-network persistent-homology diagnostic examined here —
his method's specific design choice, computing over every layer's complete
weighted graph with no subsampling step, turned out to share the same
structural immunity to a hub-selection-style sampling artifact that the
vault's other two founding NN-PH diagnostics ([[entity-tolga-birdal|Birdal]],
[[entity-asier-gutierrez-fandino|Gutiérrez-Fandiño]]) already showed.

## References
- [[claim-rieck-2019-neural-persistence-computed-without-subsampling]]
- [[observation-hub-selection-risk-needs-incomplete-object-nn-diagnostics-read-complete-record]]
- [[observation-hub-selection-artifact-absent-by-design-in-founding-ph-generalization-papers]]
- [[entity-tolga-birdal]] · [[entity-asier-gutierrez-fandino]]