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Paper Citation Record · LEDGER

Gradient descent GAN optimization is locally stable

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1706.04156.

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

pith.paper-citation-record.v1
1706.04156 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:45:26.620998Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T18:30:58.693681Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
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  • 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 b402f0db-c81f-4982-b8d9-d65113f581c8 · inbound

Control of Overfitting with Physics cites this paper.

Control of Overfitting with Physics Gradient descent GAN optimization is locally stable

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-11T15:45:26.620998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:45:26.620998Z digest=sha256:3097ec67bffc7170456bf4d08ab14c20487d92f266c45cfa3ed953b00530e395

Observation 0a7939cc-99a0-462c-a29d-cd0136c478a6 · 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 Gradient descent GAN optimization is locally stable

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T18:36:29.262130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:36:29.262130Z digest=sha256:f2bef05cabf6bb7f206c683e4b95807cb85b346aa65e46f9c3492db7c0a647de

Observation 96653c31-3b57-4bd6-b95c-e9af46190b4d · inbound

Nested Annealed Training Scheme for Generative Adversarial Networks cites this paper.

Nested Annealed Training Scheme for Generative Adversarial Networks Gradient descent GAN optimization is locally stable

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-08-10T18:30:58.698286Z

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.

source=pdf_text observed=2026-08-10T18:30:58.541245Z digest=sha256:808168a33c956d8ec54db5044ec74171de6bbfa7169f91f0cb61c088e92e445b