Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2006.15733.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:07:55.466985Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:59:40.351899Z
Reference resolution
0 of 0 outbound references displayed
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- verified fuzzy0
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- parse uncertain0
- malformed identifier0
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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 5b283630-f894-4f46-8f06-c307de3556ba · inbound
Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ? cites this paper.
Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ? Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:07:55.466985Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e95dbadf-660b-4661-9cc4-a173a9e92ae2 · inbound
Approximation Rates in Fr\'echet Metrics: Barron Spaces, Paley-Wiener Spaces, and Fourier Multipliers cites this paper.
Approximation Rates in Fr\'echet Metrics: Barron Spaces, Paley-Wiener Spaces, and Fourier Multipliers Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:09:09.766953Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0806837c-fb6e-4d3b-b2a1-0fd3c9787acd · inbound
Learn Singularly Perturbed Solutions via Homotopy Dynamics cites this paper.
Learn Singularly Perturbed Solutions via Homotopy Dynamics Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T18:57:13.596940Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 762840bb-80ed-4dc9-96e6-867398ea3e0e · inbound
Layer Separation Deep Learning Model with Auxiliary Variables for Partial Differential Equations cites this paper.
Layer Separation Deep Learning Model with Auxiliary Variables for Partial Differential Equations Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:47:35.571344Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4b0cace-70de-4d28-bd44-e4e04a2c0570 · inbound
Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization cites this paper.
Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:23:02.176801Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74560603-7629-4a9a-9392-013970fc3ef2 · inbound
Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers cites this paper.
Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:59:40.353092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.