Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z
Paper Citation Record · LEDGER
A law of robustness for two-layer neural networks with arbitrary weights
As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.07778.
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
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact7
- verified fuzzy4
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 6014b1f0-319e-424c-8059-1a5a25f22710 · outbound
This paper cites Bubeck, Y.
A law of robustness for two-layer neural networks with arbitrary weights Bubeck, Y
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T18:37:31.868417Z
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 ed96db01-b98d-45cd-bc86-a52eba8531c1 · outbound
This paper cites A Universal Law of Robustness via Isoperimetry.
A law of robustness for two-layer neural networks with arbitrary weights A Universal Law of Robustness via Isoperimetry
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-10T18:27:31.260340Z
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 603213d0-4fe4-42a5-933b-85f1209f0e7f · outbound
This paper cites an unresolved cited work.
A law of robustness for two-layer neural networks with arbitrary weights Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T18:37:31.866835Z
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 019f4460-f670-4f36-a2f7-79327fa52591 · outbound
This paper cites Dubhashi and D.
A law of robustness for two-layer neural networks with arbitrary weights Dubhashi and D
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T18:37:31.860806Z
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 bfc5986a-186f-4987-bb64-b49c6febc109 · outbound
This paper cites Thomas Hofmann, Bernhard Sch¨ olkopf, and Alexander J Smola.
A law of robustness for two-layer neural networks with arbitrary weights Thomas Hofmann, Bernhard Sch¨ olkopf, and Alexander J Smola
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-10T18:27:31.272404Z
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 95a135e9-076f-41b6-b3d8-6d4bc0db382f · outbound
This paper cites Pinkus.Ridge Functions.
A law of robustness for two-layer neural networks with arbitrary weights Pinkus.Ridge Functions
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T18:37:31.858281Z
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 0aa60333-5fcd-425d-9057-f84a62516bff · outbound
This paper cites Cambridge University Press, Cambr idge, UK (2015).
A law of robustness for two-layer neural networks with arbitrary weights Cambridge University Press, Cambr idge, UK (2015)
Reference 7
- Resolution
- verified exact
- doi, observed 2026-07-10T18:27:31.269655Z
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 fbf57cae-e01a-4b46-81b1-b08d6fe29498 · outbound
This paper cites Comput.54, 2 (2025), 193–232.
A law of robustness for two-layer neural networks with arbitrary weights Comput.54, 2 (2025), 193–232
Reference 8
- Resolution
- metadata mismatch
- doi, observed 2026-07-10T18:27:31.264164Z
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 8b34a680-0d56-4bf8-b6d6-90f70c63393d · outbound
This paper cites an unresolved cited work.
A law of robustness for two-layer neural networks with arbitrary weights Unresolved cited work
Reference 9
- Resolution
- verified exact
- doi, observed 2026-07-10T18:27:31.262124Z
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 8cdd12f7-4c08-42ee-9f95-e297fd3c2e75 · outbound
This paper cites Understanding Deep Neural Networks with Rectified Linear Units.
A law of robustness for two-layer neural networks with arbitrary weights Understanding Deep Neural Networks with Rectified Linear Units
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T18:27:31.884412Z
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 b490027e-55a1-472e-946b-f91bee075eca · outbound
This paper cites Atkinson and W.
A law of robustness for two-layer neural networks with arbitrary weights Atkinson and W
Reference 11
- Resolution
- verified exact
- doi, observed 2026-07-10T18:27:31.267712Z
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 8bc78d2f-1936-4fa1-bea5-8bfac97ba590 · outbound
This paper cites Ledoux and M.
A law of robustness for two-layer neural networks with arbitrary weights Ledoux and M
Reference 12
- Resolution
- verified exact
- doi, observed 2026-07-10T18:27:31.265952Z
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 5933af56-9911-4938-ba3e-a0ebf6463787 · outbound
This paper cites McDiarmid,On the method of bounded differences, inSurveys in Combinatorics, 1989(Norwich, 1989), London Math.
A law of robustness for two-layer neural networks with arbitrary weights McDiarmid,On the method of bounded differences, inSurveys in Combinatorics, 1989(Norwich, 1989), London Math
Reference 13
- Resolution
- verified exact
- doi, observed 2026-07-10T18:27:31.274679Z
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 65075eeb-e347-4f84-af61-02150322ef93 · outbound
This paper cites an unresolved cited work.
A law of robustness for two-layer neural networks with arbitrary weights Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T18:37:31.862636Z
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 e5adfbef-1e69-4f37-b469-ce45e76dfb38 · outbound
This paper cites Einstein Institute of Mathematics, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel Email address:yitzchak.shmalo@gmail.com.
A law of robustness for two-layer neural networks with arbitrary weights Einstein Institute of Mathematics, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel Email address:yitzchak.shmalo@gmail.com
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T18:37:31.870175Z
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.
Pith citing papers
No inbound Pith citation observations are available.