Paper Citation Record · LEDGER

Asymptotic convexity of wide and shallow neural networks

As of 18 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2507.01044.

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

pith.paper-citation-record.v1
2507.01044 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:50:42.888134Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy15
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eed148f6-53d4-42ec-87bd-7a2eafa17396 · outbound

This paper cites Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint (second edition), Hindustan Publishing Agency and Springer Nature, 2022/24.

Asymptotic convexity of wide and shallow neural networks Borkar, Stochastic Approximation: A Dynamical Systems Viewpoint (second edition), Hindustan Publishing Agency and Springer Nature, 2022/24

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.202691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 350112c2-e4ba-4796-b88c-9abd7b79ed8a · outbound

This paper cites Cagnetta, A.

Asymptotic convexity of wide and shallow neural networks Cagnetta, A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.187102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 608735cb-9594-4a3b-971b-9ab0e626b919 · outbound

This paper cites Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks.

Asymptotic convexity of wide and shallow neural networks Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.171122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6dd0e682-e7b3-4819-97bc-196bc43301db · outbound

This paper cites The convexification effect of Minkowski summation.

Asymptotic convexity of wide and shallow neural networks The convexification effect of Minkowski summation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.155694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 881c5aff-140b-49ca-9fd3-e1aa12bffd7b · outbound

This paper cites an unresolved cited work.

Asymptotic convexity of wide and shallow neural networks Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:50:43.139420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 19edc9b6-7dcd-4826-b73c-45535309b991 · outbound

This paper cites The expressive power of neural networks: a view from the width.

Asymptotic convexity of wide and shallow neural networks The expressive power of neural networks: a view from the width

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.123858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 560e5118-8dce-4b1c-a0a0-fee95c60170c · outbound

This paper cites Wide neural networks forget less catastrophically.

Asymptotic convexity of wide and shallow neural networks Wide neural networks forget less catastrophically

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.106423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f2bad9f8-ce8c-41e3-9cb4-31589cf347f1 · outbound

This paper cites The loss surface of deep and wide neural networks.

Asymptotic convexity of wide and shallow neural networks The loss surface of deep and wide neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.089844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f47392f8-a86a-49f3-8b57-ae648185b859 · outbound

This paper cites Pilanci and T.

Asymptotic convexity of wide and shallow neural networks Pilanci and T

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.073289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f2db4a87-0647-4f57-80b6-5c8778854796 · outbound

This paper cites Wide and deep neural networks achieve consistency for classification.

Asymptotic convexity of wide and shallow neural networks Wide and deep neural networks achieve consistency for classification

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.056392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74a6432a-9909-4164-8ce4-d5fe99188473 · outbound

This paper cites Convergence of Lebesgue integrals with varying measures.

Asymptotic convexity of wide and shallow neural networks Convergence of Lebesgue integrals with varying measures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.039130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a33e557-3c6c-43b9-a663-bdc734ce9b3d · outbound

This paper cites Starr’s problem.

Asymptotic convexity of wide and shallow neural networks Starr’s problem

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.019064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 08d39add-e8cf-432e-8ada-f7067b226590 · outbound

This paper cites Quasi-equilibria in markets with non-convex preferences.

Asymptotic convexity of wide and shallow neural networks Quasi-equilibria in markets with non-convex preferences

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:43.001545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5adc5666-c396-4dc0-87b2-b5940995abe4 · outbound

This paper cites Approximation of points of the convex hull of a sum of sets by points of the sum: an elementary approach.

Asymptotic convexity of wide and shallow neural networks Approximation of points of the convex hull of a sum of sets by points of the sum: an elementary approach

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:42.985880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6413254f-6aa3-4442-a6c5-7155980fc871 · outbound

This paper cites Some comments on a theorem of Hardy and Littlewood.

Asymptotic convexity of wide and shallow neural networks Some comments on a theorem of Hardy and Littlewood

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:42.968602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4635801a-8205-41ba-a227-c9e0643c6f41 · outbound

This paper cites Do wider neural networks really help adversarial robustness?.

Asymptotic convexity of wide and shallow neural networks Do wider neural networks really help adversarial robustness?

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:42.951703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5bc9257-f2a5-4fea-9b49-43d03d44e941 · outbound

This paper cites Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation.

Asymptotic convexity of wide and shallow neural networks Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:42.888134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.