Paper Citation Record · LEDGER

On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2005.13530.

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

pith.paper-citation-record.v1
2005.13530 v1

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

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:00.129590Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

14
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d0b62bd6-ffdf-4dd8-850b-74bb7f353a30 · inbound

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs cites this paper.

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-24T11:29:24.903532Z

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 064d0a4b-9a0b-4ce9-a5ac-6bb0e04fee23 · inbound

Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization cites this paper.

Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:00.129590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42fc4b3e-d2e5-4091-8f20-2116a06a05f7 · inbound

Large Spikes in Stochastic Gradient Descent: A Large-Deviations View cites this paper.

Large Spikes in Stochastic Gradient Descent: A Large-Deviations View On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:30:51.060625Z

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 c8280f8c-efcd-4422-a42e-38f12a7dec69 · inbound

On the global convergence of gradient flow for wide shallow models beyond homogeneous nonlinearities cites this paper.

On the global convergence of gradient flow for wide shallow models beyond homogeneous nonlinearities On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:51:19.329483Z

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 b523a32e-8bda-4e75-8aa1-6142930be8b0 · inbound

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias cites this paper.

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:53:51.164849Z

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 cb3fdb8b-d009-4e40-8906-3fe3a00384c0 · inbound

Singular perturbations and hierarchical learning in two-layer neural networks cites this paper.

Singular perturbations and hierarchical learning in two-layer neural networks On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-14T08:40:30.522134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfdbe00d-fccb-471e-abf3-1e1d2d311897 · inbound

Elliptic Regularity Theory in Barron Spaces and Applications to the Deep Ritz Method cites this paper.

Elliptic Regularity Theory in Barron Spaces and Applications to the Deep Ritz Method On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T01:28:52.356953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.