Paper Citation Record · LEDGER

Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1808.09540.

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

pith.paper-citation-record.v1
1808.09540 v4

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 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:47:49.269104Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:20:54.474696Z

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 dd70150a-5a7b-4657-8070-5ab2c0e96d1e · inbound

A Tunable Despeckling Neural Network Stabilized via Diffusion Equation cites this paper.

A Tunable Despeckling Neural Network Stabilized via Diffusion Equation Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T13:47:49.269104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6841dcb3-1836-448b-91b7-71eb32dea3a0 · inbound

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs cites this paper.

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T18:36:29.271112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ab00353-faaf-45d7-a0e2-543bf09ecc56 · inbound

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies cites this paper.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T21:31:51.795561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 793d2e4d-aa03-4ecc-b723-cbdf7d7a9a1b · inbound

Generalization error bounds for two-layer neural networks with Lipschitz loss function cites this paper.

Generalization error bounds for two-layer neural networks with Lipschitz loss function Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:20:54.482535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.