Paper Citation Record · LEDGER

Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2312.05840.

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

pith.paper-citation-record.v1
2312.05840 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:17:57.303980Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:48:23.077546Z

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 ac88a013-b33d-4d69-8fdb-86adb61e9df1 · inbound

TOAST: Transformer Optimization using Adaptive and Simple Transformations cites this paper.

TOAST: Transformer Optimization using Adaptive and Simple Transformations Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:48:23.081650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8442059-ac0d-49f4-af54-8bad3dd6a97f · inbound

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

Recursive Computation of Path Homology for Stratified Digraphs Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:44:24.319355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2640d2a-cf67-4981-9a72-24fa6051c719 · inbound

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

Topology of Out-of-Distribution Examples in Deep Neural Networks Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T17:10:14.799012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eb3f9b5-255f-4e3e-bc0d-8545b232c9b4 · inbound

Topological Signatures of Grokking cites this paper.

Topological Signatures of Grokking Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:13.712220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6908241c-fcd5-41f3-8dee-6798f2428a0e · inbound

Topology-Enhanced Alignment for Large Language Models: Trajectory Topology Loss and Topological Preference Optimization cites this paper.

Topology-Enhanced Alignment for Large Language Models: Trajectory Topology Loss and Topological Preference Optimization Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:35:57.798592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f213bdf-4b65-449d-8131-dd33b15a4eac · inbound

How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule cites this paper.

How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T15:17:57.303980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.