Paper Citation Record · LEDGER

Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2305.18502.

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

pith.paper-citation-record.v1
2305.18502 v2

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 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:55:22.490803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:37:13.876180Z

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 dfd91b96-aa6b-42f3-a0ed-c67b7b7bc402 · inbound

Optimal Spectral Transitions in High-Dimensional Multi-Index Models cites this paper.

Optimal Spectral Transitions in High-Dimensional Multi-Index Models Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T11:55:22.490803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36740c57-7e59-4273-9f19-6c677d0e6c77 · inbound

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime cites this paper.

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:17.462661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d17721ae-5d02-4f0f-9450-4f67944ed222 · inbound

Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks cites this paper.

Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:28:06.718982Z

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 3deaaf47-5052-460e-a0ad-e1e6cfc1161f · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:51:05.706065Z

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 b7b3b7d6-ccb1-4767-98c2-27c0cabc990e · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:25:54.619324Z

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 a51ded95-aed4-4915-b667-dbf7f72a1c78 · inbound

A Theory of Saddle Escape in Deep Nonlinear Networks cites this paper.

A Theory of Saddle Escape in Deep Nonlinear Networks Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:45:12.075792Z

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 0c12e504-3e16-466b-9ee7-2fdf6b43d442 · inbound

Homogenization of $\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent cites this paper.

Homogenization of $\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:37:13.878042Z

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.