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Paper Citation Record · LEDGER

Theoretically Principled Trade-off between Robustness and Accuracy

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1901.08573.

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

pith.paper-citation-record.v1
1901.08573 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:15:41.519448Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T22:26:17.803257Z

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 6a1fb0a3-4c08-4262-aefa-24d728b06504 · inbound

Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning cites this paper.

Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning Theoretically Principled Trade-off between Robustness and Accuracy

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-05-24T21:14:57.058679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-24T21:12:17.698880Z digest=sha256:b2ac0bd0a7448b83dcf4222d20eacda19005ac404b2c8ccc29c20a3835cc7d13

Observation 955dff57-3fc0-4224-bd44-2fc3390c3856 · inbound

Does Order Matter : Connecting The Law of Robustness to Robust Generalization cites this paper.

Does Order Matter : Connecting The Law of Robustness to Robust Generalization Theoretically Principled Trade-off between Robustness and Accuracy

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-02T21:15:41.519448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T21:15:41.519448Z digest=sha256:609cd5521485a7bc98cbdacabe0162ae9ec9a3e2547a14863a75efcbf38a447b

Observation 53f0872b-1ea0-4be2-8e0a-dbe70fe0f064 · inbound

Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing cites this paper.

Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing Theoretically Principled Trade-off between Robustness and Accuracy

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:39:48.153558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T00:39:43.196010Z digest=sha256:b890bf95aae09e99abdcb278c30ccfbdff99ba2166c96cd9ba1e529d57656501

Observation 73147e3e-cb1a-4541-b974-4d52b5496b9e · inbound

Laundering AI Authority with Adversarial Examples cites this paper.

Laundering AI Authority with Adversarial Examples Theoretically Principled Trade-off between Robustness and Accuracy

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:41:08.000930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-08T17:19:38.662062Z digest=sha256:96fc4a6ae9fe7b6a3e4f2d94170ff9b31d54f845c32caa4e04abf1a903e13130

Observation 66410df3-e7c1-4573-9384-3d9cb017ed88 · inbound

Landseer: Exploring the Machine Learning Defense Landscape cites this paper.

Landseer: Exploring the Machine Learning Defense Landscape Theoretically Principled Trade-off between Robustness and Accuracy

Reference 121

Resolution
metadata mismatch
local_arxiv, observed 2026-06-29T17:33:45.414241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-29T17:27:29.241219Z digest=sha256:6e08cbfae627df728c5d72055bb145300fccc286534588b1b48349ebc65226ac

Observation 333314b3-c550-4fbb-bfc2-c1236a5cd3cb · inbound

SORA: Free Second-Order Attacks in Fast Adversarial Training cites this paper.

SORA: Free Second-Order Attacks in Fast Adversarial Training Theoretically Principled Trade-off between Robustness and Accuracy

Reference 9

Resolution
metadata mismatch
local_arxiv, observed 2026-06-28T19:12:35.104164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:05:06.136594Z digest=sha256:28507b9404087cc37072faa7e344bd0bca3b607e4f3b4ea4999b46fca2ed1ff8

Observation d3a055d5-061e-4508-901d-e97a545e299b · 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 Theoretically Principled Trade-off between Robustness and Accuracy

Reference 57

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T22:26:17.804389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T15:23:26.461740Z digest=sha256:17e25da2f38bceff8506893e268111cd3ca25a89318257ff9f6403288b068b26