Paper Citation Record · LEDGER

Smooth Adversarial Training

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2006.14536.

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

pith.paper-citation-record.v1
2006.14536 v2

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 2026-08-11T18:06:13.070414Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T20:45:27.499761Z

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 ca7c9294-080a-404d-957d-b2c692a14f04 · inbound

Unsolved Problems in ML Safety cites this paper.

Unsolved Problems in ML Safety Smooth Adversarial Training

Reference 205

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:45:27.502156Z

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.

Observation 74f33b9b-fedd-4ff8-8e5d-79ff8df74399 · inbound

How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? cites this paper.

How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? Smooth Adversarial Training

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T18:06:13.070414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa2e97c2-258b-401c-99c3-b030a050cb02 · inbound

Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations cites this paper.

Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations Smooth Adversarial Training

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T10:37:12.669555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ba25560-467f-470d-82f3-48886cb40ec1 · inbound

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks cites this paper.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Smooth Adversarial Training

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.674523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f7d27c0-5fc7-4d29-9214-aba618109788 · inbound

A Composite Activation Function for Learning Stable Binary Representations cites this paper.

A Composite Activation Function for Learning Stable Binary Representations Smooth Adversarial Training

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:07:07.882428Z

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.