Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1909.12292.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T09:36:50.772999Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T12:55:43.879145Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation ad24a13c-39e8-4a48-bf5e-ff299f0eacb4 · inbound
Convergence of difference inclusions via a diameter criterion Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Reference 72
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a11cbe5f-076a-4f6a-a36a-31c4aea9d7ed · inbound
Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Reference 29
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 74a4a7a9-8180-45dd-8515-8053025fc876 · inbound
Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Reference 29
Source-reported events for the cited work
Unavailable: canonical work link unavailable.