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Paper Citation Record · LEDGER

Characterizing the Decision Boundary of Deep Neural Networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1912.11460.

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

pith.paper-citation-record.v1
1912.11460 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:21:27.572891Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:24:53.326967Z

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 e5009e84-0cb0-4c3b-bb2c-910903a3795f · inbound

Dataset Ownership Verification in Contrastive Pre-trained Models cites this paper.

Dataset Ownership Verification in Contrastive Pre-trained Models Characterizing the Decision Boundary of Deep Neural Networks

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-08T13:21:27.572891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:21:27.572891Z digest=sha256:ec1075ae204a91a16975ed71566340cf80eba2494b0dad36453ce90aacf97ea4

Observation ba0d7ae1-0595-41dc-b616-f347ee058694 · inbound

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary cites this paper.

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary Characterizing the Decision Boundary of Deep Neural Networks

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-22T13:24:53.329683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-22T13:22:37.107679Z digest=sha256:67ce90d71f3b02416c4e330fd6c193027f2d8eaee0db777e26dabd332f1d264b

Observation 16de7c72-e78d-4e5e-a4c2-f86e082ac737 · inbound

The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning cites this paper.

The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning Characterizing the Decision Boundary of Deep Neural Networks

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T20:20:17.736278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-15T20:18:20.610845Z digest=sha256:dcf234f0493f5f1e483c56853dd45f9d652fbac776e36690eaf7649a67240070

Observation 971c7238-bc08-40e8-b8e5-5662664bbf70 · inbound

On the Decompositionality of Neural Networks cites this paper.

On the Decompositionality of Neural Networks Characterizing the Decision Boundary of Deep Neural Networks

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:55:57.074243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T17:53:49.835875Z digest=sha256:2b110dfffbd7879a05020c5f562790ba28b5fb90cfc76e145b0cc3a2349ae01c

Observation dfb31d04-11cc-46db-ab2d-3627ce846c3f · inbound

Fast and Lightweight Backdoor Detection via Head Random Probing cites this paper.

Fast and Lightweight Backdoor Detection via Head Random Probing Characterizing the Decision Boundary of Deep Neural Networks

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T12:43:17.529648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-20T12:39:19.973387Z digest=sha256:0d85cb34251c84ff7d94a8dd0a22cbd9a69037a79b7411f5ddd35ead438729a4