Paper Citation Record · LEDGER

Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1905.11675.

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

pith.paper-citation-record.v1
1905.11675 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:49:00.196171Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T15:23:32.764084Z

Reference resolution

0 of 0 outbound references displayed

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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 e1a7367f-3f7e-4917-ab79-c3e1861516b4 · inbound

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms cites this paper.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T14:49:00.196171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e0f7a8-1ee6-4d7f-b130-18bf2594f235 · inbound

On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime cites this paper.

On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:23:09.977676Z

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 e595744f-80a8-47c0-b135-c1ab2a5103ec · inbound

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit cites this paper.

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:23.817611Z

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 9692b529-6fc6-4075-85b4-26bbe1ebbc19 · inbound

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit cites this paper.

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:51.026805Z

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 11b0a501-6b88-4a75-8a58-d38508f3be66 · inbound

Canonical Regularisation of Wide Feature-Learning Neural Networks cites this paper.

Canonical Regularisation of Wide Feature-Learning Neural Networks Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:32:54.032826Z

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 eaa0e1f4-8f1e-4c74-845a-93dc03c39771 · inbound

Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework cites this paper.

Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T15:23:32.765692Z

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 c68d25f1-5815-4713-9477-ebfe1b02c012 · inbound

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers cites this paper.

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-01T06:09:01.051615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.