Paper Citation Record · LEDGER

Lipschitz regularity of deep neural networks: analysis and efficient estimation

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1805.10965.

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

pith.paper-citation-record.v1
1805.10965 v2

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 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:00:40.031029Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T18:36:29.551354Z

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 d3eb3546-9195-451f-8997-4ee4c94d1b63 · inbound

Sequential data assimilation for PDEs using shape-morphing solutions cites this paper.

Sequential data assimilation for PDEs using shape-morphing solutions Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T13:00:40.031029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f622327-475d-4812-932f-b466101c9e36 · 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 regularity of deep neural networks: analysis and efficient estimation

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T18:36:29.556119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f04c974-e784-48ce-97cb-bc6af00fcede · 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 Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T21:15:40.639040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fef3639-3060-41a4-8535-32a9794c64ca · inbound

MOSAIC: Masked Outsourcing of Secure AI Computations cites this paper.

MOSAIC: Masked Outsourcing of Secure AI Computations Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T11:38:48.170449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.