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Paper Citation Record · LEDGER

Improving Adversarial Robustness of Ensembles with Diversity Training

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1901.09981.

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

pith.paper-citation-record.v1
1901.09981 v1

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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:46:57.347353Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:27:47.866741Z

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 c912392c-b3c6-4864-b546-ca79718ddddb · inbound

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data cites this paper.

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data Improving Adversarial Robustness of Ensembles with Diversity Training

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-11T20:46:57.347353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:46:57.347353Z digest=sha256:f3c3d74c949abe4bbd7ff91b37dcfac5012a2f692d5a0fcd96b3f8de9949b88d

Observation 12cf5c06-83d8-4f65-8b39-c3c0e6f4eb5a · inbound

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation cites this paper.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Improving Adversarial Robustness of Ensembles with Diversity Training

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T14:51:09.738105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:51:09.738105Z digest=sha256:304656afce0fcff9276322faac68d7680515e6623f03c5240e1b6cddaa4afbd3

Observation 095eaf16-9be1-45ec-a2ed-1f8a7e2d5bbc · inbound

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss cites this paper.

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Improving Adversarial Robustness of Ensembles with Diversity Training

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T11:49:51.235837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:49:51.235837Z digest=sha256:09f5e75900bbf5d20c647042f372fdb858bcc45a3452d2dfc42cb373e3d7c681

Observation a7c10f41-6e4b-43d3-b12b-b8fd1eec84c3 · inbound

Learning from Peers: Collaborative Ensemble Adversarial Training cites this paper.

Learning from Peers: Collaborative Ensemble Adversarial Training Improving Adversarial Robustness of Ensembles with Diversity Training

Reference 7

Resolution
metadata mismatch
local_arxiv, observed 2026-08-05T15:27:47.873138Z

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=2026-08-05T15:27:47.697986Z digest=sha256:33e4309466ad5917ee0304f22fd1d7bda0fb15a57f6d7ca681c9e10ce7338fcd