Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1905.11675.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:40.439889Z
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
- 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 e9f04798-4a48-4bab-a702-aa64104d58f7 · inbound
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning cites this paper.
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:47:40.439889Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.