Paper Citation Record · LEDGER

Randomized Smoothing of All Shapes and Sizes

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2002.08118.

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

pith.paper-citation-record.v1
2002.08118 v5

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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:14.214660Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:33:45.420544Z

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 36851c63-39bb-40c1-b45a-c16d74973bb4 · inbound

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise cites this paper.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Randomized Smoothing of All Shapes and Sizes

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:14.214660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31fb1cd9-aa58-4c4c-a31c-3ac5dd246f74 · inbound

Laundering AI Authority with Adversarial Examples cites this paper.

Laundering AI Authority with Adversarial Examples Randomized Smoothing of All Shapes and Sizes

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:08.048633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5d51154d-7c64-4e95-ab55-19e958a0a7c6 · inbound

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy cites this paper.

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy Randomized Smoothing of All Shapes and Sizes

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:11:21.119447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5a44a528-f4ae-4e64-b6a3-8849543efc46 · inbound

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

Landseer: Exploring the Machine Learning Defense Landscape Randomized Smoothing of All Shapes and Sizes

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:33:45.422137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.