Paper Citation Record · LEDGER

The Dimpled Manifold Model of Adversarial Examples in Machine Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2106.10151.

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

pith.paper-citation-record.v1
2106.10151 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:57:29.025297Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:26:17.829107Z

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 c42ea062-6169-464b-bd7f-271ae07e2600 · inbound

Generalizability vs. Counterfactual Explainability Trade-Off cites this paper.

Generalizability vs. Counterfactual Explainability Trade-Off The Dimpled Manifold Model of Adversarial Examples in Machine Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:29.025297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57200a7f-d426-47ad-9be1-63678b0f5843 · inbound

Adversarial Examples Are Not Bugs, They Are Superposition cites this paper.

Adversarial Examples Are Not Bugs, They Are Superposition The Dimpled Manifold Model of Adversarial Examples in Machine Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:10.874588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4666ec04-2d62-4eb6-86d5-737b3813d051 · inbound

Nearest Neighbor Projection Removal Adversarial Training cites this paper.

Nearest Neighbor Projection Removal Adversarial Training The Dimpled Manifold Model of Adversarial Examples in Machine Learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:52:46.232192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dfe25805-4cc7-4584-a6ee-6998dcba6dda · inbound

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs cites this paper.

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs The Dimpled Manifold Model of Adversarial Examples in Machine Learning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:26:17.830507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.