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Paper Citation Record · LEDGER

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1812.09764.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1812.09764 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:44:24.589702Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T07:59:39.625974Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 97a859bb-2df5-4051-bd9a-717eeb5be781 · inbound

Recursive Computation of Path Homology for Stratified Digraphs cites this paper.

Recursive Computation of Path Homology for Stratified Digraphs Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T20:44:24.589702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:44:24.589702Z digest=sha256:c98c4c3e1a8a0045a6722f99e60af5d9237dcfc9b6610c1bd8404ac251925fea

Observation 20a8ea79-f35f-4907-87bb-ed8d285df64d · inbound

Topology of Out-of-Distribution Examples in Deep Neural Networks cites this paper.

Topology of Out-of-Distribution Examples in Deep Neural Networks Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-10T17:10:14.833726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:10:14.833726Z digest=sha256:1ff8660ac1d9b44ff7fc552ba25420dc67def4a75fef45af611cc8a947af90cb

Observation 77f6d260-6ab2-4d96-8b70-e157a5984b1a · inbound

A Quotient Homology Theory of Representation in Neural Networks cites this paper.

A Quotient Homology Theory of Representation in Neural Networks Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-09T15:39:50.447896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T15:39:50.447896Z digest=sha256:4327c453196f4bc3391c1e018079a7cd431f03067f86eab5afb6a6510c1fa3c7

Observation dc88f46f-175d-446a-8e2e-88ae768e1e41 · inbound

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data cites this paper.

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T15:39:05.063555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:39:05.063555Z digest=sha256:e546ec527b63818588e921f80b3036571f6cab167cc88db33621877db67214ec

Observation ca22721c-8ec1-4160-9a3c-c54cc0894c7d · inbound

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension cites this paper.

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:22:33.238566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-18T10:22:28.096542Z digest=sha256:2e9e1715836fabcf1dceedbc90cef16d4097a540119144b2d646833c129c1383

Observation 5df84db9-fee6-4bb2-a99f-6f5ee434394f · inbound

Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation cites this paper.

Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-14T21:37:04.895721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T21:37:04.895721Z digest=sha256:40d95162ae8aabfaed40a4e4b03f86b854089030d4679ca33a04db984f3cfee8

Observation ff0f1d6f-d452-47f2-bc97-ba588875cfdf · inbound

Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction cites this paper.

Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-10T08:48:02.057487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T08:43:00.282970Z digest=sha256:23706780c2e2cd7676df480e2a89090ccb0bdbe3bfd8bd878b5e8b8a59429230

Observation 1ec09933-8840-4c4a-b2f7-6248d5106d66 · inbound

TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection cites this paper.

TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:26:24.521713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-12T01:09:31.633709Z digest=sha256:1c69a851b2f24f63023621b06834d4a80f5c82256c406273571e162652f46cc3

Observation 3e94c990-fd98-469f-8af8-f7992c0ca549 · inbound

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts cites this paper.

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-15T05:55:04.784943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-15T05:54:32.496951Z digest=sha256:3dce1a61192bd678bc199a7d9b01967c01441bc55a22bc6a26719063424eef65

Observation 652072d7-51d5-4bc8-893c-a88367280f22 · inbound

A Three Axis Evaluation Framework for Mapper Algorithms cites this paper.

A Three Axis Evaluation Framework for Mapper Algorithms Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-07-04T07:59:39.627531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-26T12:30:34.551724Z digest=sha256:046391435a1de9f854d1b6c1de13671fda8446d15ef650c2dcfb305b01fa852d