Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1808.09540.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T05:37:26.026152Z
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 53f94442-9707-4297-9b4a-8614cb5bc437 · inbound
Metric Learning for Adversarial Robustness cites this paper.
Metric Learning for Adversarial Robustness Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:37:26.026152Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-23T06:30:58.430688+00:00.